Arguments of the submit script#

Note

One can load, modify, and export the input file by using our effective web-based tool DP-GUI online or hosted using the command line interface dpgen2 gui. All parameters below can be set in DP-GUI. By clicking “SAVE JSON”, one can download the input file.

dflow_config:#: type: dict | NoneType, optional, default: None
argument path: dflow_config
The configuration passed to dflow

dflow_s3_config:#: type: dict | NoneType, optional, default: None
argument path: dflow_s3_config
The S3 configuration passed to dflow

default_step_config:#

type: dict, optional, default: {}
argument path: default_step_config
The default step configuration.
template_config:#
type: dict, optional, default: {'image': 'dptechnology/dpgen2:latest'}
argument path: default_step_config/template_config
The configs passed to the PythonOPTemplate.
image:#
type: str, optional, default: dptechnology/dpgen2:latest
argument path: default_step_config/template_config/image
The image to run the step.
timeout:#
type: NoneType | int, optional, default: None
argument path: default_step_config/template_config/timeout
The time limit of the OP. Unit is second.
retry_on_transient_error:#
type: NoneType | int, optional, default: None
argument path: default_step_config/template_config/retry_on_transient_error
The number of retry times if a TransientError is raised.
timeout_as_transient_error:#
type: bool, optional, default: False
argument path: default_step_config/template_config/timeout_as_transient_error
Treat the timeout as TransientError.
envs:#
type: dict | NoneType, optional, default: None
argument path: default_step_config/template_config/envs
The environmental variables.
template_slice_config:#
type: dict, optional
argument path: default_step_config/template_slice_config
The configs passed to the Slices.
group_size:#
type: NoneType | int, optional, default: None
argument path: default_step_config/template_slice_config/group_size
The number of tasks running on a single node. It is efficient for a large number of short tasks.
pool_size:#
type: NoneType | int, optional, default: None
argument path: default_step_config/template_slice_config/pool_size
The number of tasks running at the same time on one node.
continue_on_failed:#
type: bool, optional, default: False
argument path: default_step_config/continue_on_failed
If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).
continue_on_num_success:#
type: NoneType | int, optional, default: None
argument path: default_step_config/continue_on_num_success
Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.
continue_on_success_ratio:#
type: NoneType | float, optional, default: None
argument path: default_step_config/continue_on_success_ratio
Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.
parallelism:#
type: NoneType | int, optional, default: None
argument path: default_step_config/parallelism
The parallelism for the step
executor:#
type: dict | NoneType, optional, default: None
argument path: default_step_config/executor
The executor of the step.
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: default_step_config/executor/type
possible choices: dispatcher
The type of the executor.
When type is set to dispatcher:

bohrium_config:#

type: dict | NoneType, optional, default: None
argument path: bohrium_config
Configurations for the Bohrium platform.
username:#
type: str
argument path: bohrium_config/username
The username of the Bohrium platform
password:#
type: str
argument path: bohrium_config/password
The password of the Bohrium platform
project_id:#
type: int
argument path: bohrium_config/project_id
The project ID of the Bohrium platform
host:#
type: str, optional, default: https://workflows.deepmodeling.com
argument path: bohrium_config/host
The host name of the Bohrium platform. Will overwrite dflow_config[‘host’]
k8s_api_server:#
type: str, optional, default: https://workflows.deepmodeling.com
argument path: bohrium_config/k8s_api_server
The k8s server of the Bohrium platform. Will overwrite dflow_config[‘k8s_api_server’]
repo_key:#
type: str, optional, default: oss-bohrium
argument path: bohrium_config/repo_key
The repo key of the Bohrium platform. Will overwrite dflow_s3_config[‘repo_key’]
storage_client:#
type: str, optional, default: dflow.plugins.bohrium.TiefblueClient
argument path: bohrium_config/storage_client
The storage client of the Bohrium platform. Will overwrite dflow_s3_config[‘storage_client’]

step_configs:#

type: dict, optional, default: {}
argument path: step_configs
Configurations for executing dflow steps
prep_train_config:#
type: dict, optional, default: {'template_config': {'image': 'dptechnology/dpgen2:latest', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}
argument path: step_configs/prep_train_config
Configuration for prepare train
template_config:#
type: dict, optional, default: {'image': 'dptechnology/dpgen2:latest'}
argument path: step_configs/prep_train_config/template_config
The configs passed to the PythonOPTemplate.
image:#
type: str, optional, default: dptechnology/dpgen2:latest
argument path: step_configs/prep_train_config/template_config/image
The image to run the step.
timeout:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_train_config/template_config/timeout
The time limit of the OP. Unit is second.
retry_on_transient_error:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_train_config/template_config/retry_on_transient_error
The number of retry times if a TransientError is raised.
timeout_as_transient_error:#
type: bool, optional, default: False
argument path: step_configs/prep_train_config/template_config/timeout_as_transient_error
Treat the timeout as TransientError.
envs:#
type: dict | NoneType, optional, default: None
argument path: step_configs/prep_train_config/template_config/envs
The environmental variables.
template_slice_config:#
type: dict, optional
argument path: step_configs/prep_train_config/template_slice_config
The configs passed to the Slices.
group_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_train_config/template_slice_config/group_size
The number of tasks running on a single node. It is efficient for a large number of short tasks.
pool_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_train_config/template_slice_config/pool_size
The number of tasks running at the same time on one node.
continue_on_failed:#
type: bool, optional, default: False
argument path: step_configs/prep_train_config/continue_on_failed
If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).
continue_on_num_success:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_train_config/continue_on_num_success
Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.
continue_on_success_ratio:#
type: NoneType | float, optional, default: None
argument path: step_configs/prep_train_config/continue_on_success_ratio
Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.
parallelism:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_train_config/parallelism
The parallelism for the step
executor:#
type: dict | NoneType, optional, default: None
argument path: step_configs/prep_train_config/executor
The executor of the step.
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: step_configs/prep_train_config/executor/type
possible choices: dispatcher
The type of the executor.
When type is set to dispatcher:
run_train_config:#
type: dict, optional, default: {'template_config': {'image': 'dptechnology/dpgen2:latest', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}
argument path: step_configs/run_train_config
Configuration for run train
template_config:#
type: dict, optional, default: {'image': 'dptechnology/dpgen2:latest'}
argument path: step_configs/run_train_config/template_config
The configs passed to the PythonOPTemplate.
image:#
type: str, optional, default: dptechnology/dpgen2:latest
argument path: step_configs/run_train_config/template_config/image
The image to run the step.
timeout:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_train_config/template_config/timeout
The time limit of the OP. Unit is second.
retry_on_transient_error:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_train_config/template_config/retry_on_transient_error
The number of retry times if a TransientError is raised.
timeout_as_transient_error:#
type: bool, optional, default: False
argument path: step_configs/run_train_config/template_config/timeout_as_transient_error
Treat the timeout as TransientError.
envs:#
type: dict | NoneType, optional, default: None
argument path: step_configs/run_train_config/template_config/envs
The environmental variables.
template_slice_config:#
type: dict, optional
argument path: step_configs/run_train_config/template_slice_config
The configs passed to the Slices.
group_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_train_config/template_slice_config/group_size
The number of tasks running on a single node. It is efficient for a large number of short tasks.
pool_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_train_config/template_slice_config/pool_size
The number of tasks running at the same time on one node.
continue_on_failed:#
type: bool, optional, default: False
argument path: step_configs/run_train_config/continue_on_failed
If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).
continue_on_num_success:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_train_config/continue_on_num_success
Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.
continue_on_success_ratio:#
type: NoneType | float, optional, default: None
argument path: step_configs/run_train_config/continue_on_success_ratio
Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.
parallelism:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_train_config/parallelism
The parallelism for the step
executor:#
type: dict | NoneType, optional, default: None
argument path: step_configs/run_train_config/executor
The executor of the step.
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: step_configs/run_train_config/executor/type
possible choices: dispatcher
The type of the executor.
When type is set to dispatcher:
prep_explore_config:#
type: dict, optional, default: {'template_config': {'image': 'dptechnology/dpgen2:latest', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}
argument path: step_configs/prep_explore_config
Configuration for prepare exploration
template_config:#
type: dict, optional, default: {'image': 'dptechnology/dpgen2:latest'}
argument path: step_configs/prep_explore_config/template_config
The configs passed to the PythonOPTemplate.
image:#
type: str, optional, default: dptechnology/dpgen2:latest
argument path: step_configs/prep_explore_config/template_config/image
The image to run the step.
timeout:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_explore_config/template_config/timeout
The time limit of the OP. Unit is second.
retry_on_transient_error:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_explore_config/template_config/retry_on_transient_error
The number of retry times if a TransientError is raised.
timeout_as_transient_error:#
type: bool, optional, default: False
argument path: step_configs/prep_explore_config/template_config/timeout_as_transient_error
Treat the timeout as TransientError.
envs:#
type: dict | NoneType, optional, default: None
argument path: step_configs/prep_explore_config/template_config/envs
The environmental variables.
template_slice_config:#
type: dict, optional
argument path: step_configs/prep_explore_config/template_slice_config
The configs passed to the Slices.
group_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_explore_config/template_slice_config/group_size
The number of tasks running on a single node. It is efficient for a large number of short tasks.
pool_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_explore_config/template_slice_config/pool_size
The number of tasks running at the same time on one node.
continue_on_failed:#
type: bool, optional, default: False
argument path: step_configs/prep_explore_config/continue_on_failed
If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).
continue_on_num_success:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_explore_config/continue_on_num_success
Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.
continue_on_success_ratio:#
type: NoneType | float, optional, default: None
argument path: step_configs/prep_explore_config/continue_on_success_ratio
Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.
parallelism:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_explore_config/parallelism
The parallelism for the step
executor:#
type: dict | NoneType, optional, default: None
argument path: step_configs/prep_explore_config/executor
The executor of the step.
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: step_configs/prep_explore_config/executor/type
possible choices: dispatcher
The type of the executor.
When type is set to dispatcher:
run_explore_config:#
type: dict, optional, default: {'template_config': {'image': 'dptechnology/dpgen2:latest', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}
argument path: step_configs/run_explore_config
Configuration for run exploration
template_config:#
type: dict, optional, default: {'image': 'dptechnology/dpgen2:latest'}
argument path: step_configs/run_explore_config/template_config
The configs passed to the PythonOPTemplate.
image:#
type: str, optional, default: dptechnology/dpgen2:latest
argument path: step_configs/run_explore_config/template_config/image
The image to run the step.
timeout:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_explore_config/template_config/timeout
The time limit of the OP. Unit is second.
retry_on_transient_error:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_explore_config/template_config/retry_on_transient_error
The number of retry times if a TransientError is raised.
timeout_as_transient_error:#
type: bool, optional, default: False
argument path: step_configs/run_explore_config/template_config/timeout_as_transient_error
Treat the timeout as TransientError.
envs:#
type: dict | NoneType, optional, default: None
argument path: step_configs/run_explore_config/template_config/envs
The environmental variables.
template_slice_config:#
type: dict, optional
argument path: step_configs/run_explore_config/template_slice_config
The configs passed to the Slices.
group_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_explore_config/template_slice_config/group_size
The number of tasks running on a single node. It is efficient for a large number of short tasks.
pool_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_explore_config/template_slice_config/pool_size
The number of tasks running at the same time on one node.
continue_on_failed:#
type: bool, optional, default: False
argument path: step_configs/run_explore_config/continue_on_failed
If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).
continue_on_num_success:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_explore_config/continue_on_num_success
Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.
continue_on_success_ratio:#
type: NoneType | float, optional, default: None
argument path: step_configs/run_explore_config/continue_on_success_ratio
Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.
parallelism:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_explore_config/parallelism
The parallelism for the step
executor:#
type: dict | NoneType, optional, default: None
argument path: step_configs/run_explore_config/executor
The executor of the step.
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: step_configs/run_explore_config/executor/type
possible choices: dispatcher
The type of the executor.
When type is set to dispatcher:
prep_fp_config:#
type: dict, optional, default: {'template_config': {'image': 'dptechnology/dpgen2:latest', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}
argument path: step_configs/prep_fp_config
Configuration for prepare fp
template_config:#
type: dict, optional, default: {'image': 'dptechnology/dpgen2:latest'}
argument path: step_configs/prep_fp_config/template_config
The configs passed to the PythonOPTemplate.
image:#
type: str, optional, default: dptechnology/dpgen2:latest
argument path: step_configs/prep_fp_config/template_config/image
The image to run the step.
timeout:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_fp_config/template_config/timeout
The time limit of the OP. Unit is second.
retry_on_transient_error:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_fp_config/template_config/retry_on_transient_error
The number of retry times if a TransientError is raised.
timeout_as_transient_error:#
type: bool, optional, default: False
argument path: step_configs/prep_fp_config/template_config/timeout_as_transient_error
Treat the timeout as TransientError.
envs:#
type: dict | NoneType, optional, default: None
argument path: step_configs/prep_fp_config/template_config/envs
The environmental variables.
template_slice_config:#
type: dict, optional
argument path: step_configs/prep_fp_config/template_slice_config
The configs passed to the Slices.
group_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_fp_config/template_slice_config/group_size
The number of tasks running on a single node. It is efficient for a large number of short tasks.
pool_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_fp_config/template_slice_config/pool_size
The number of tasks running at the same time on one node.
continue_on_failed:#
type: bool, optional, default: False
argument path: step_configs/prep_fp_config/continue_on_failed
If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).
continue_on_num_success:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_fp_config/continue_on_num_success
Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.
continue_on_success_ratio:#
type: NoneType | float, optional, default: None
argument path: step_configs/prep_fp_config/continue_on_success_ratio
Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.
parallelism:#
type: NoneType | int, optional, default: None
argument path: step_configs/prep_fp_config/parallelism
The parallelism for the step
executor:#
type: dict | NoneType, optional, default: None
argument path: step_configs/prep_fp_config/executor
The executor of the step.
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: step_configs/prep_fp_config/executor/type
possible choices: dispatcher
The type of the executor.
When type is set to dispatcher:
run_fp_config:#
type: dict, optional, default: {'template_config': {'image': 'dptechnology/dpgen2:latest', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}
argument path: step_configs/run_fp_config
Configuration for run fp
template_config:#
type: dict, optional, default: {'image': 'dptechnology/dpgen2:latest'}
argument path: step_configs/run_fp_config/template_config
The configs passed to the PythonOPTemplate.
image:#
type: str, optional, default: dptechnology/dpgen2:latest
argument path: step_configs/run_fp_config/template_config/image
The image to run the step.
timeout:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_fp_config/template_config/timeout
The time limit of the OP. Unit is second.
retry_on_transient_error:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_fp_config/template_config/retry_on_transient_error
The number of retry times if a TransientError is raised.
timeout_as_transient_error:#
type: bool, optional, default: False
argument path: step_configs/run_fp_config/template_config/timeout_as_transient_error
Treat the timeout as TransientError.
envs:#
type: dict | NoneType, optional, default: None
argument path: step_configs/run_fp_config/template_config/envs
The environmental variables.
template_slice_config:#
type: dict, optional
argument path: step_configs/run_fp_config/template_slice_config
The configs passed to the Slices.
group_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_fp_config/template_slice_config/group_size
The number of tasks running on a single node. It is efficient for a large number of short tasks.
pool_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_fp_config/template_slice_config/pool_size
The number of tasks running at the same time on one node.
continue_on_failed:#
type: bool, optional, default: False
argument path: step_configs/run_fp_config/continue_on_failed
If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).
continue_on_num_success:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_fp_config/continue_on_num_success
Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.
continue_on_success_ratio:#
type: NoneType | float, optional, default: None
argument path: step_configs/run_fp_config/continue_on_success_ratio
Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.
parallelism:#
type: NoneType | int, optional, default: None
argument path: step_configs/run_fp_config/parallelism
The parallelism for the step
executor:#
type: dict | NoneType, optional, default: None
argument path: step_configs/run_fp_config/executor
The executor of the step.
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: step_configs/run_fp_config/executor/type
possible choices: dispatcher
The type of the executor.
When type is set to dispatcher:
select_confs_config:#
type: dict, optional, default: {'template_config': {'image': 'dptechnology/dpgen2:latest', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}
argument path: step_configs/select_confs_config
Configuration for the select confs
template_config:#
type: dict, optional, default: {'image': 'dptechnology/dpgen2:latest'}
argument path: step_configs/select_confs_config/template_config
The configs passed to the PythonOPTemplate.
image:#
type: str, optional, default: dptechnology/dpgen2:latest
argument path: step_configs/select_confs_config/template_config/image
The image to run the step.
timeout:#
type: NoneType | int, optional, default: None
argument path: step_configs/select_confs_config/template_config/timeout
The time limit of the OP. Unit is second.
retry_on_transient_error:#
type: NoneType | int, optional, default: None
argument path: step_configs/select_confs_config/template_config/retry_on_transient_error
The number of retry times if a TransientError is raised.
timeout_as_transient_error:#
type: bool, optional, default: False
argument path: step_configs/select_confs_config/template_config/timeout_as_transient_error
Treat the timeout as TransientError.
envs:#
type: dict | NoneType, optional, default: None
argument path: step_configs/select_confs_config/template_config/envs
The environmental variables.
template_slice_config:#
type: dict, optional
argument path: step_configs/select_confs_config/template_slice_config
The configs passed to the Slices.
group_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/select_confs_config/template_slice_config/group_size
The number of tasks running on a single node. It is efficient for a large number of short tasks.
pool_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/select_confs_config/template_slice_config/pool_size
The number of tasks running at the same time on one node.
continue_on_failed:#
type: bool, optional, default: False
argument path: step_configs/select_confs_config/continue_on_failed
If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).
continue_on_num_success:#
type: NoneType | int, optional, default: None
argument path: step_configs/select_confs_config/continue_on_num_success
Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.
continue_on_success_ratio:#
type: NoneType | float, optional, default: None
argument path: step_configs/select_confs_config/continue_on_success_ratio
Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.
parallelism:#
type: NoneType | int, optional, default: None
argument path: step_configs/select_confs_config/parallelism
The parallelism for the step
executor:#
type: dict | NoneType, optional, default: None
argument path: step_configs/select_confs_config/executor
The executor of the step.
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: step_configs/select_confs_config/executor/type
possible choices: dispatcher
The type of the executor.
When type is set to dispatcher:
collect_data_config:#
type: dict, optional, default: {'template_config': {'image': 'dptechnology/dpgen2:latest', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}
argument path: step_configs/collect_data_config
Configuration for the collect data
template_config:#
type: dict, optional, default: {'image': 'dptechnology/dpgen2:latest'}
argument path: step_configs/collect_data_config/template_config
The configs passed to the PythonOPTemplate.
image:#
type: str, optional, default: dptechnology/dpgen2:latest
argument path: step_configs/collect_data_config/template_config/image
The image to run the step.
timeout:#
type: NoneType | int, optional, default: None
argument path: step_configs/collect_data_config/template_config/timeout
The time limit of the OP. Unit is second.
retry_on_transient_error:#
type: NoneType | int, optional, default: None
argument path: step_configs/collect_data_config/template_config/retry_on_transient_error
The number of retry times if a TransientError is raised.
timeout_as_transient_error:#
type: bool, optional, default: False
argument path: step_configs/collect_data_config/template_config/timeout_as_transient_error
Treat the timeout as TransientError.
envs:#
type: dict | NoneType, optional, default: None
argument path: step_configs/collect_data_config/template_config/envs
The environmental variables.
template_slice_config:#
type: dict, optional
argument path: step_configs/collect_data_config/template_slice_config
The configs passed to the Slices.
group_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/collect_data_config/template_slice_config/group_size
The number of tasks running on a single node. It is efficient for a large number of short tasks.
pool_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/collect_data_config/template_slice_config/pool_size
The number of tasks running at the same time on one node.
continue_on_failed:#
type: bool, optional, default: False
argument path: step_configs/collect_data_config/continue_on_failed
If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).
continue_on_num_success:#
type: NoneType | int, optional, default: None
argument path: step_configs/collect_data_config/continue_on_num_success
Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.
continue_on_success_ratio:#
type: NoneType | float, optional, default: None
argument path: step_configs/collect_data_config/continue_on_success_ratio
Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.
parallelism:#
type: NoneType | int, optional, default: None
argument path: step_configs/collect_data_config/parallelism
The parallelism for the step
executor:#
type: dict | NoneType, optional, default: None
argument path: step_configs/collect_data_config/executor
The executor of the step.
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: step_configs/collect_data_config/executor/type
possible choices: dispatcher
The type of the executor.
When type is set to dispatcher:
cl_step_config:#
type: dict, optional, default: {'template_config': {'image': 'dptechnology/dpgen2:latest', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}
argument path: step_configs/cl_step_config
Configuration for the concurrent learning step
template_config:#
type: dict, optional, default: {'image': 'dptechnology/dpgen2:latest'}
argument path: step_configs/cl_step_config/template_config
The configs passed to the PythonOPTemplate.
image:#
type: str, optional, default: dptechnology/dpgen2:latest
argument path: step_configs/cl_step_config/template_config/image
The image to run the step.
timeout:#
type: NoneType | int, optional, default: None
argument path: step_configs/cl_step_config/template_config/timeout
The time limit of the OP. Unit is second.
retry_on_transient_error:#
type: NoneType | int, optional, default: None
argument path: step_configs/cl_step_config/template_config/retry_on_transient_error
The number of retry times if a TransientError is raised.
timeout_as_transient_error:#
type: bool, optional, default: False
argument path: step_configs/cl_step_config/template_config/timeout_as_transient_error
Treat the timeout as TransientError.
envs:#
type: dict | NoneType, optional, default: None
argument path: step_configs/cl_step_config/template_config/envs
The environmental variables.
template_slice_config:#
type: dict, optional
argument path: step_configs/cl_step_config/template_slice_config
The configs passed to the Slices.
group_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/cl_step_config/template_slice_config/group_size
The number of tasks running on a single node. It is efficient for a large number of short tasks.
pool_size:#
type: NoneType | int, optional, default: None
argument path: step_configs/cl_step_config/template_slice_config/pool_size
The number of tasks running at the same time on one node.
continue_on_failed:#
type: bool, optional, default: False
argument path: step_configs/cl_step_config/continue_on_failed
If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).
continue_on_num_success:#
type: NoneType | int, optional, default: None
argument path: step_configs/cl_step_config/continue_on_num_success
Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.
continue_on_success_ratio:#
type: NoneType | float, optional, default: None
argument path: step_configs/cl_step_config/continue_on_success_ratio
Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.
parallelism:#
type: NoneType | int, optional, default: None
argument path: step_configs/cl_step_config/parallelism
The parallelism for the step
executor:#
type: dict | NoneType, optional, default: None
argument path: step_configs/cl_step_config/executor
The executor of the step.
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: step_configs/cl_step_config/executor/type
possible choices: dispatcher
The type of the executor.
When type is set to dispatcher:

upload_python_packages:#: type: str | typing.List[str] | NoneType, optional, default: None, alias: upload_python_package
argument path: upload_python_packages
Upload python package, for debug purpose

inputs:#

type: dict
argument path: inputs
The input parameter and artifacts for dpgen2
type_map:#
type: typing.List[str]
argument path: inputs/type_map
The type map. e.g. [“Al”, “Mg”]. Al and Mg will have type 0 and 1, respectively.
mass_map:#
type: typing.List[float]
argument path: inputs/mass_map
The mass map. e.g. [27., 24.]. Al and Mg will be set with mass 27. and 24. amu, respectively.
init_data_prefix:#
type: str | NoneType, optional, default: None
argument path: inputs/init_data_prefix
The prefix of initial data systems
mixed_type:#
type: bool, optional, default: False
argument path: inputs/mixed_type
Use deepmd/npy/mixed format for storing training data.
do_finetune:#
type: bool, optional, default: False
argument path: inputs/do_finetune
Finetune the pretrained model during the first iteration. If it is set to True, then in the first iteration, the internal flag finetune_mode is set to “finetune”. In this step, we finetune the pretrained model in the train step. After that, in the following iterations, init_model_policy is forced to be “yes”, the flag finetune_mode is set as “no”, which means we use –init-frz-model or –init-model to train based on models from the previous iteration. The “do_finetune” flag is set to False by default, while the internal flag finetune_mode is set to “no”, which means anything related to finetuning will not be done.
init_data_sys:#
type: str | typing.List[str] | NoneType, optional, default: None
argument path: inputs/init_data_sys
The inital data systems
init_data_uri:#
type: str | NoneType, optional, default: None
argument path: inputs/init_data_uri
The URI of initial data
multitask:#
type: bool, optional, default: False
argument path: inputs/multitask
Do multitask training
head:#
type: str | NoneType, optional, default: None
argument path: inputs/head
Head to use in the multitask training
multi_init_data:#
type: dict | NoneType, optional, default: None
argument path: inputs/multi_init_data
The inital data for multitask, it should be a dict, whose keys are task names and each value is a dictcontaining fields prefix and sys for initial data of each task
multi_init_data_uri:#
type: str | NoneType, optional, default: None
argument path: inputs/multi_init_data_uri
The URI of initial data for multitask
valid_data_prefix:#
type: str | NoneType, optional, default: None
argument path: inputs/valid_data_prefix
The prefix of validation data systems
valid_data_sys:#
type: str | typing.List[str] | NoneType, optional, default: None
argument path: inputs/valid_data_sys
The validation data systems
valid_data_uri:#
type: str | NoneType, optional, default: None
argument path: inputs/valid_data_uri
The URI of validation data
use_ele_temp:#
type: int, optional, default: 0
argument path: inputs/use_ele_temp
Whether to use electronic temperature, 0 for no, 1 for frame temperature, and 2 for atomic temperature
multi_valid_data:#
type: dict | NoneType, optional, default: None
argument path: inputs/multi_valid_data
The validation data for multitask, it should be a dict, whose keys are task names and each value is a dictcontaining fields prefix and sys for initial data of each task
multi_valid_data_uri:#
type: str | NoneType, optional, default: None
argument path: inputs/multi_valid_data_uri
The URI of validation data for multitask

train:#

type: dict
argument path: train
The configuration for training
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: train/type
possible choices: dp, dp-dist
the type of the training
When type is set to dp:
config:#
type: dict, optional, default: {'command': 'dp', 'impl': 'tensorflow', 'init_model_policy': 'no', 'init_model_old_ratio': 0.9, 'init_model_numb_steps': 400000, 'init_model_start_lr': 0.0001, 'init_model_start_pref_e': 0.1, 'init_model_start_pref_f': 100, 'init_model_start_pref_v': 0.0, 'init_model_with_finetune': False, 'finetune_args': '', 'multitask': False, 'head': None, 'train_args': '', 'split_last_iter_valid_ratio': None}
argument path: train[dp]/config
Number of models trained for evaluating the model deviation
command:#
type: str, optional, default: dp
argument path: train[dp]/config/command
The command for DP, ‘dp’ for default
impl:#
type: str, optional, default: tensorflow, alias: backend
argument path: train[dp]/config/impl
The implementation/backend of DP. It can be ‘tensorflow’ or ‘pytorch’. ‘tensorflow’ for default.
init_model_policy:#
type: str, optional, default: no
argument path: train[dp]/config/init_model_policy
The policy of init-model training. It can be
‘no’: No init-model training. Traing from scratch.
‘yes’: Do init-model training.
‘old_data_larger_than:XXX’: Do init-model if the training data size of the previous model is larger than XXX. XXX is an int number.
init_model_old_ratio:#
type: float, optional, default: 0.9
argument path: train[dp]/config/init_model_old_ratio
The frequency ratio of old data over new data
init_model_numb_steps:#
type: int, optional, default: 400000, alias: init_model_stop_batch
argument path: train[dp]/config/init_model_numb_steps
The number of training steps when init-model
init_model_start_lr:#
type: float, optional, default: 0.0001
argument path: train[dp]/config/init_model_start_lr
The start learning rate when init-model
init_model_start_pref_e:#
type: float, optional, default: 0.1
argument path: train[dp]/config/init_model_start_pref_e
The start energy prefactor in loss when init-model
init_model_start_pref_f:#
type: float, optional, default: 100
argument path: train[dp]/config/init_model_start_pref_f
The start force prefactor in loss when init-model
init_model_start_pref_v:#
type: float, optional, default: 0.0
argument path: train[dp]/config/init_model_start_pref_v
The start virial prefactor in loss when init-model
init_model_with_finetune:#
type: bool, optional, default: False
argument path: train[dp]/config/init_model_with_finetune
Use finetune for init model
finetune_args:#
type: str, optional, default: (empty string)
argument path: train[dp]/config/finetune_args
Extra arguments for finetuning
multitask:#
type: bool, optional, default: False
argument path: train[dp]/config/multitask
Do multitask training
head:#
type: str | NoneType, optional, default: None
argument path: train[dp]/config/head
Head to use in the multitask training
train_args:#
type: str, optional, default: (empty string)
argument path: train[dp]/config/train_args
Extra arguments for dp train
split_last_iter_valid_ratio:#
type: NoneType | float, optional, default: None
argument path: train[dp]/config/split_last_iter_valid_ratio
Ratio of valid data if split data of last iter
numb_models:#
type: int, optional, default: 4
argument path: train[dp]/numb_models
Number of models trained for evaluating the model deviation
template_script:#
type: str | typing.List[str]
argument path: train[dp]/template_script
File names of the template training script. It can be a List[str], the length of which is the same as numb_models. Each template script in the list is used to train a model. Can be a str, the models share the same template training script.
init_models_paths:#
type: typing.List[str] | NoneType, optional, default: None, alias: training_iter0_model_path
argument path: train[dp]/init_models_paths
the paths to initial models
init_models_uri:#
type: str | NoneType, optional, default: None
argument path: train[dp]/init_models_uri
The URI of initial models
optional_files:#
type: list | NoneType, optional, default: None
argument path: train[dp]/optional_files
Optional files for training
When type is set to dp-dist:
config:#
type: dict, optional, default: {'command': 'dp', 'impl': 'tensorflow', 'init_model_policy': 'no', 'init_model_old_ratio': 0.9, 'init_model_numb_steps': 400000, 'init_model_start_lr': 0.0001, 'init_model_start_pref_e': 0.1, 'init_model_start_pref_f': 100, 'init_model_start_pref_v': 0.0, 'init_model_with_finetune': False, 'finetune_args': '', 'multitask': False, 'head': None, 'train_args': '', 'split_last_iter_valid_ratio': None}
argument path: train[dp-dist]/config
Configuration of training
command:#
type: str, optional, default: dp
argument path: train[dp-dist]/config/command
The command for DP, ‘dp’ for default
impl:#
type: str, optional, default: tensorflow, alias: backend
argument path: train[dp-dist]/config/impl
The implementation/backend of DP. It can be ‘tensorflow’ or ‘pytorch’. ‘tensorflow’ for default.
init_model_policy:#
type: str, optional, default: no
argument path: train[dp-dist]/config/init_model_policy
The policy of init-model training. It can be
‘no’: No init-model training. Traing from scratch.
‘yes’: Do init-model training.
‘old_data_larger_than:XXX’: Do init-model if the training data size of the previous model is larger than XXX. XXX is an int number.
init_model_old_ratio:#
type: float, optional, default: 0.9
argument path: train[dp-dist]/config/init_model_old_ratio
The frequency ratio of old data over new data
init_model_numb_steps:#
type: int, optional, default: 400000, alias: init_model_stop_batch
argument path: train[dp-dist]/config/init_model_numb_steps
The number of training steps when init-model
init_model_start_lr:#
type: float, optional, default: 0.0001
argument path: train[dp-dist]/config/init_model_start_lr
The start learning rate when init-model
init_model_start_pref_e:#
type: float, optional, default: 0.1
argument path: train[dp-dist]/config/init_model_start_pref_e
The start energy prefactor in loss when init-model
init_model_start_pref_f:#
type: float, optional, default: 100
argument path: train[dp-dist]/config/init_model_start_pref_f
The start force prefactor in loss when init-model
init_model_start_pref_v:#
type: float, optional, default: 0.0
argument path: train[dp-dist]/config/init_model_start_pref_v
The start virial prefactor in loss when init-model
init_model_with_finetune:#
type: bool, optional, default: False
argument path: train[dp-dist]/config/init_model_with_finetune
Use finetune for init model
finetune_args:#
type: str, optional, default: (empty string)
argument path: train[dp-dist]/config/finetune_args
Extra arguments for finetuning
multitask:#
type: bool, optional, default: False
argument path: train[dp-dist]/config/multitask
Do multitask training
head:#
type: str | NoneType, optional, default: None
argument path: train[dp-dist]/config/head
Head to use in the multitask training
train_args:#
type: str, optional, default: (empty string)
argument path: train[dp-dist]/config/train_args
Extra arguments for dp train
split_last_iter_valid_ratio:#
type: NoneType | float, optional, default: None
argument path: train[dp-dist]/config/split_last_iter_valid_ratio
Ratio of valid data if split data of last iter
template_script:#
type: str | typing.List[str]
argument path: train[dp-dist]/template_script
File names of the template training script. It can be a List[str], the length of which is the same as numb_models. Each template script in the list is used to train a model. Can be a str, the models share the same template training script.
student_model_path:#
type: str, optional
argument path: train[dp-dist]/student_model_path
The path of student model
student_model_uri:#
type: str | NoneType, optional, default: None
argument path: train[dp-dist]/student_model_uri
The URI of student model
optional_files:#
type: list | NoneType, optional, default: None
argument path: train[dp-dist]/optional_files
Optional files for training

explore:#

type: dict
argument path: explore
The configuration for exploration
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: explore/type
possible choices: lmp, calypso, calypso:default, calypso:merge, diffcsp
The type of the exploration
lmp: The exploration by LAMMPS simulations
calypso: The exploration by CALYPSO structure prediction
calypso:default: The exploration by CALYPSO structure prediction
calypso:merge: The exploration by CALYPSO structure prediction
diffcsp: The exploration by DiffCSP
When type is set to lmp:
The exploration by LAMMPS simulations
config:#
type: dict, optional, default: {'command': 'lmp', 'teacher_model_path': None, 'shuffle_models': False, 'head': None, 'use_ele_temp': 0, 'model_frozen_head': None, 'use_hdf5': False, 'extra_output_files': []}
argument path: explore[lmp]/config
Configuration of lmp exploration
command:#
type: str, optional, default: lmp
argument path: explore[lmp]/config/command
The command of LAMMPS
teacher_model_path:#
type: str | NoneType | BinaryFileInput, optional, default: None
argument path: explore[lmp]/config/teacher_model_path
The teacher model in Knowledge Distillation
shuffle_models:#
type: bool, optional, default: False
argument path: explore[lmp]/config/shuffle_models
Randomly pick a model from the group of models to drive theexploration MD simulation
head:#
type: str | NoneType, optional, default: None
argument path: explore[lmp]/config/head
Select a head from multitask
use_ele_temp:#
type: int, optional, default: 0
argument path: explore[lmp]/config/use_ele_temp
Whether to use electronic temperature, 0 for no, 1 for frame temperature, and 2 for atomic temperature
model_frozen_head:#
type: str | NoneType, optional, default: None
argument path: explore[lmp]/config/model_frozen_head
Select a head from multitask
use_hdf5:#
type: bool, optional, default: False
argument path: explore[lmp]/config/use_hdf5
Use HDF5 to store trajs and model_devis
extra_output_files:#
type: list, optional, default: []
argument path: explore[lmp]/config/extra_output_files
Extra output file names, support wildcards
max_numb_iter:#
type: int, optional, default: 10
argument path: explore[lmp]/max_numb_iter
Maximum number of iterations per stage
fatal_at_max:#
type: bool, optional, default: True
argument path: explore[lmp]/fatal_at_max
Fatal when the number of iteration per stage reaches the max_numb_iter
output_nopbc:#
type: bool, optional, default: False
argument path: explore[lmp]/output_nopbc
Remove pbc of the output configurations
convergence:#
type: dict
argument path: explore[lmp]/convergence
The method of convergence check.
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: explore[lmp]/convergence/type
possible choices: fixed-levels, fixed-levels-max-select, adaptive-lower
the type of the condidate selection and convergence check method.
fixed-levels: The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations will be randomly sampled from candidates for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
fixed-levels-max-select: The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations with maximal model deviation in the candidates are sent for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
adaptive-lower: The method of adaptive adjust the lower trust levels. In each step of iterations, a number (set by numb_candi_f or numb_candi_v) or a ratio (set by rate_candi_f or rate_candi_v) of configurations with a model deviation lower than the higher trust level (level_f_hi, level_v_hi) are treated as candidates. The lowest model deviation of the candidates are treated as the lower trust level. If the lower trust level does not change significant (controlled by conv_tolerance) in n_checked_steps, the stage is treated as converged.
When type is set to fixed-levels:
The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations will be randomly sampled from candidates for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
level_f_lo:#
type: float
argument path: explore[lmp]/convergence[fixed-levels]/level_f_lo
The lower trust level of force model deviation
level_f_hi:#
type: float
argument path: explore[lmp]/convergence[fixed-levels]/level_f_hi
The higher trust level of force model deviation
level_v_lo:#
type: NoneType | float, optional, default: None
argument path: explore[lmp]/convergence[fixed-levels]/level_v_lo
The lower trust level of virial model deviation
level_v_hi:#
type: NoneType | float, optional, default: None
argument path: explore[lmp]/convergence[fixed-levels]/level_v_hi
The higher trust level of virial model deviation
conv_accuracy:#
type: float, optional, default: 0.9
argument path: explore[lmp]/convergence[fixed-levels]/conv_accuracy
If the ratio of accurate frames is larger than this value, the stage is converged
When type is set to fixed-levels-max-select:
The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations with maximal model deviation in the candidates are sent for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
level_f_lo:#
type: float
argument path: explore[lmp]/convergence[fixed-levels-max-select]/level_f_lo
The lower trust level of force model deviation
level_f_hi:#
type: float
argument path: explore[lmp]/convergence[fixed-levels-max-select]/level_f_hi
The higher trust level of force model deviation
level_v_lo:#
type: NoneType | float, optional, default: None
argument path: explore[lmp]/convergence[fixed-levels-max-select]/level_v_lo
The lower trust level of virial model deviation
level_v_hi:#
type: NoneType | float, optional, default: None
argument path: explore[lmp]/convergence[fixed-levels-max-select]/level_v_hi
The higher trust level of virial model deviation
conv_accuracy:#
type: float, optional, default: 0.9
argument path: explore[lmp]/convergence[fixed-levels-max-select]/conv_accuracy
If the ratio of accurate frames is larger than this value, the stage is converged
When type is set to adaptive-lower:
The method of adaptive adjust the lower trust levels. In each step of iterations, a number (set by numb_candi_f or numb_candi_v) or a ratio (set by rate_candi_f or rate_candi_v) of configurations with a model deviation lower than the higher trust level (level_f_hi, level_v_hi) are treated as candidates. The lowest model deviation of the candidates are treated as the lower trust level. If the lower trust level does not change significant (controlled by conv_tolerance) in n_checked_steps, the stage is treated as converged.
level_f_hi:#
type: float, optional, default: 0.5
argument path: explore[lmp]/convergence[adaptive-lower]/level_f_hi
The higher trust level of force model deviation
numb_candi_f:#
type: int, optional, default: 200
argument path: explore[lmp]/convergence[adaptive-lower]/numb_candi_f
The number of force frames that has a model deviation lower than level_f_hi treated as candidate.
rate_candi_f:#
type: float, optional, default: 0.01
argument path: explore[lmp]/convergence[adaptive-lower]/rate_candi_f
The ratio of force frames that has a model deviation lower than level_f_hi treated as candidate.
level_v_hi:#
type: NoneType | float, optional, default: None
argument path: explore[lmp]/convergence[adaptive-lower]/level_v_hi
The higher trust level of virial model deviation
numb_candi_v:#
type: int, optional, default: 0
argument path: explore[lmp]/convergence[adaptive-lower]/numb_candi_v
The number of virial frames that has a model deviation lower than level_v_hi treated as candidate.
rate_candi_v:#
type: float, optional, default: 0.0
argument path: explore[lmp]/convergence[adaptive-lower]/rate_candi_v
The ratio of virial frames that has a model deviation lower than level_v_hi treated as candidate.
n_checked_steps:#
type: int, optional, default: 2
argument path: explore[lmp]/convergence[adaptive-lower]/n_checked_steps
The number of steps to check the convergence.
conv_tolerance:#
type: float, optional, default: 0.05
argument path: explore[lmp]/convergence[adaptive-lower]/conv_tolerance
The convergence tolerance.
candi_sel_prob:#
type: str, optional, default: uniform
argument path: explore[lmp]/convergence[adaptive-lower]/candi_sel_prob
The method for selecting candidates. It can be ‘uniform’: all candidates are of the same probability. ‘inv_pop_f’ or ‘inv_pop_f:nhist’: the probability is inversely propotional to the population of a histogram between leven_f_lo and level_f_hi. The number of bins in the histogram is set by nhist, which should be an integer. The default is 10.
configurations:#
type: list, alias: configuration
argument path: explore[lmp]/configurations
A list of initial configurations.
This argument takes a list with each element containing the following:
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: explore[lmp]/configurations/type
possible choices: alloy, file
the type of the initial configuration generator.
alloy: Generate alloys with a certain lattice or user proided structure, the elements randomly occuping the lattice with user provided probability .
file: Generate alloys from user provided file(s). The file(s) are assume to be load by dpdata.
When type is set to alloy:
Generate alloys with a certain lattice or user proided structure, the elements randomly occuping the lattice with user provided probability .
numb_confs:#
type: int, optional, default: 1
argument path: explore[lmp]/configurations[alloy]/numb_confs
The number of configurations to generate
lattice:#
type: list | tuple
argument path: explore[lmp]/configurations[alloy]/lattice
The lattice. Should be a list providing [ “lattice_type”, lattice_const ], or a list providing [ “/path/to/dpdata/system”, “fmt” ]. The two styles are distinguished by the type of the second element. Currently “lattice_type” can be “bcc”, “fcc”, “hcp”, “sc” or “diamond”.
replicate:#
type: list | NoneType, optional, default: None
argument path: explore[lmp]/configurations[alloy]/replicate
The number of replicates in each direction
concentration:#
type: list | NoneType, optional, default: None
argument path: explore[lmp]/configurations[alloy]/concentration
The concentration of each element. List[List[float]] or List[float] or None. If List[float], the concentrations of each element. The length of the list should be the same as the type_map. If List[List[float]], a list of concentrations (List[float]) is randomly picked from the List. If None, the elements are assumed to be of equal concentration.
cell_pert_frac:#
type: float, optional, default: 0.0
argument path: explore[lmp]/configurations[alloy]/cell_pert_frac
The faction of cell perturbation
atom_pert_dist:#
type: float, optional, default: 0.0
argument path: explore[lmp]/configurations[alloy]/atom_pert_dist
The distance of atomic position perturbation
When type is set to file:
Generate alloys from user provided file(s). The file(s) are assume to be load by dpdata.
files:#
type: str | list
argument path: explore[lmp]/configurations[file]/files
The paths to the configuration files. widecards are supported.
prefix:#
type: str | NoneType, optional, default: None
argument path: explore[lmp]/configurations[file]/prefix
The prefix of file paths.
fmt:#
type: str, optional, default: auto
argument path: explore[lmp]/configurations[file]/fmt
The format (dpdata accepted formats) of the files.
remove_pbc:#
type: bool, optional, default: False
argument path: explore[lmp]/configurations[file]/remove_pbc
The remove the pbc of the data. shift the coords to the center of box so it can be used with lammps.
stages:#
type: typing.List[typing.List[dict]]
argument path: explore[lmp]/stages
The definition of exploration stages of type List[List[ExplorationTaskGroup]. The outer list provides the enumeration of the exploration stages. Then each stage is defined by a list of exploration task groups. Each task group is described in the task group definition
filters:#
type: list | dict, optional, default: []
argument path: explore[lmp]/filters
A list of configuration filters
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: explore[lmp]/filters/type
possible choices: distance, box_skewness, box_length
the type of the configuration filter.
distance: Configuration filter of type distance
box_skewness: Configuration filter of type box_skewness
box_length: Configuration filter of type box_length
When type is set to distance:
Configuration filter of type distance
max_workers:#
type: NoneType | int, optional, default: None
argument path: explore[lmp]/filters[distance]/max_workers
The maximum number of processes used to filter configurations, None represents as many as the processors of the machine, and 1 for serial
custom_safe_dist:#
type: dict, optional, default: {}
argument path: explore[lmp]/filters[distance]/custom_safe_dist
Custom safe distance (in unit of bohr) for each element
safe_dist_ratio:#
type: float, optional, default: 1.0
argument path: explore[lmp]/filters[distance]/safe_dist_ratio
The ratio multiplied to the safe distance
When type is set to box_skewness:
Configuration filter of type box_skewness
max_workers:#
type: NoneType | int, optional, default: None
argument path: explore[lmp]/filters[box_skewness]/max_workers
The maximum number of processes used to filter configurations, None represents as many as the processors of the machine, and 1 for serial
theta:#
type: float, optional, default: 60.0
argument path: explore[lmp]/filters[box_skewness]/theta
The threshold for angles between the edges of the cell. If all angles are larger than this value the check is passed
When type is set to box_length:
Configuration filter of type box_length
max_workers:#
type: NoneType | int, optional, default: None
argument path: explore[lmp]/filters[box_length]/max_workers
The maximum number of processes used to filter configurations, None represents as many as the processors of the machine, and 1 for serial
length_ratio:#
type: float, optional, default: 5.0
argument path: explore[lmp]/filters[box_length]/length_ratio
The threshold for the length ratio between the edges of the cell. If all length ratios are smaller than this value the check is passed
When type is set to calypso:
The exploration by CALYPSO structure prediction
config:#
type: dict, optional, default: {'command': 'lmp', 'teacher_model_path': None, 'shuffle_models': False, 'head': None, 'use_ele_temp': 0, 'model_frozen_head': None, 'use_hdf5': False, 'extra_output_files': []}
argument path: explore[calypso]/config
Configuration of calypso exploration
model_devi_group_size:#
type: int, optional
argument path: explore[calypso]/config/model_devi_group_size
group size for model deviation.
run_calypso_command:#
type: str, optional, default: calypso.x
argument path: explore[calypso]/config/run_calypso_command
command of running calypso.
run_opt_command:#
type: str, optional
argument path: explore[calypso]/config/run_opt_command
command of running optimization with dp.
max_numb_iter:#
type: int, optional, default: 10
argument path: explore[calypso]/max_numb_iter
Maximum number of iterations per stage
fatal_at_max:#
type: bool, optional, default: True
argument path: explore[calypso]/fatal_at_max
Fatal when the number of iteration per stage reaches the max_numb_iter
output_nopbc:#
type: bool, optional, default: False
argument path: explore[calypso]/output_nopbc
Remove pbc of the output configurations
convergence:#
type: dict
argument path: explore[calypso]/convergence
The method of convergence check.
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: explore[calypso]/convergence/type
possible choices: fixed-levels, fixed-levels-max-select, adaptive-lower
the type of the condidate selection and convergence check method.
fixed-levels: The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations will be randomly sampled from candidates for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
fixed-levels-max-select: The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations with maximal model deviation in the candidates are sent for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
adaptive-lower: The method of adaptive adjust the lower trust levels. In each step of iterations, a number (set by numb_candi_f or numb_candi_v) or a ratio (set by rate_candi_f or rate_candi_v) of configurations with a model deviation lower than the higher trust level (level_f_hi, level_v_hi) are treated as candidates. The lowest model deviation of the candidates are treated as the lower trust level. If the lower trust level does not change significant (controlled by conv_tolerance) in n_checked_steps, the stage is treated as converged.
When type is set to fixed-levels:
The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations will be randomly sampled from candidates for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
level_f_lo:#
type: float
argument path: explore[calypso]/convergence[fixed-levels]/level_f_lo
The lower trust level of force model deviation
level_f_hi:#
type: float
argument path: explore[calypso]/convergence[fixed-levels]/level_f_hi
The higher trust level of force model deviation
level_v_lo:#
type: NoneType | float, optional, default: None
argument path: explore[calypso]/convergence[fixed-levels]/level_v_lo
The lower trust level of virial model deviation
level_v_hi:#
type: NoneType | float, optional, default: None
argument path: explore[calypso]/convergence[fixed-levels]/level_v_hi
The higher trust level of virial model deviation
conv_accuracy:#
type: float, optional, default: 0.9
argument path: explore[calypso]/convergence[fixed-levels]/conv_accuracy
If the ratio of accurate frames is larger than this value, the stage is converged
When type is set to fixed-levels-max-select:
The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations with maximal model deviation in the candidates are sent for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
level_f_lo:#
type: float
argument path: explore[calypso]/convergence[fixed-levels-max-select]/level_f_lo
The lower trust level of force model deviation
level_f_hi:#
type: float
argument path: explore[calypso]/convergence[fixed-levels-max-select]/level_f_hi
The higher trust level of force model deviation
level_v_lo:#
type: NoneType | float, optional, default: None
argument path: explore[calypso]/convergence[fixed-levels-max-select]/level_v_lo
The lower trust level of virial model deviation
level_v_hi:#
type: NoneType | float, optional, default: None
argument path: explore[calypso]/convergence[fixed-levels-max-select]/level_v_hi
The higher trust level of virial model deviation
conv_accuracy:#
type: float, optional, default: 0.9
argument path: explore[calypso]/convergence[fixed-levels-max-select]/conv_accuracy
If the ratio of accurate frames is larger than this value, the stage is converged
When type is set to adaptive-lower:
The method of adaptive adjust the lower trust levels. In each step of iterations, a number (set by numb_candi_f or numb_candi_v) or a ratio (set by rate_candi_f or rate_candi_v) of configurations with a model deviation lower than the higher trust level (level_f_hi, level_v_hi) are treated as candidates. The lowest model deviation of the candidates are treated as the lower trust level. If the lower trust level does not change significant (controlled by conv_tolerance) in n_checked_steps, the stage is treated as converged.
level_f_hi:#
type: float, optional, default: 0.5
argument path: explore[calypso]/convergence[adaptive-lower]/level_f_hi
The higher trust level of force model deviation
numb_candi_f:#
type: int, optional, default: 200
argument path: explore[calypso]/convergence[adaptive-lower]/numb_candi_f
The number of force frames that has a model deviation lower than level_f_hi treated as candidate.
rate_candi_f:#
type: float, optional, default: 0.01
argument path: explore[calypso]/convergence[adaptive-lower]/rate_candi_f
The ratio of force frames that has a model deviation lower than level_f_hi treated as candidate.
level_v_hi:#
type: NoneType | float, optional, default: None
argument path: explore[calypso]/convergence[adaptive-lower]/level_v_hi
The higher trust level of virial model deviation
numb_candi_v:#
type: int, optional, default: 0
argument path: explore[calypso]/convergence[adaptive-lower]/numb_candi_v
The number of virial frames that has a model deviation lower than level_v_hi treated as candidate.
rate_candi_v:#
type: float, optional, default: 0.0
argument path: explore[calypso]/convergence[adaptive-lower]/rate_candi_v
The ratio of virial frames that has a model deviation lower than level_v_hi treated as candidate.
n_checked_steps:#
type: int, optional, default: 2
argument path: explore[calypso]/convergence[adaptive-lower]/n_checked_steps
The number of steps to check the convergence.
conv_tolerance:#
type: float, optional, default: 0.05
argument path: explore[calypso]/convergence[adaptive-lower]/conv_tolerance
The convergence tolerance.
candi_sel_prob:#
type: str, optional, default: uniform
argument path: explore[calypso]/convergence[adaptive-lower]/candi_sel_prob
The method for selecting candidates. It can be ‘uniform’: all candidates are of the same probability. ‘inv_pop_f’ or ‘inv_pop_f:nhist’: the probability is inversely propotional to the population of a histogram between leven_f_lo and level_f_hi. The number of bins in the histogram is set by nhist, which should be an integer. The default is 10.
configurations:#
type: list, alias: configuration
argument path: explore[calypso]/configurations
A list of initial configurations.
This argument takes a list with each element containing the following:
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: explore[calypso]/configurations/type
possible choices: alloy, file
the type of the initial configuration generator.
alloy: Generate alloys with a certain lattice or user proided structure, the elements randomly occuping the lattice with user provided probability .
file: Generate alloys from user provided file(s). The file(s) are assume to be load by dpdata.
When type is set to alloy:
Generate alloys with a certain lattice or user proided structure, the elements randomly occuping the lattice with user provided probability .
numb_confs:#
type: int, optional, default: 1
argument path: explore[calypso]/configurations[alloy]/numb_confs
The number of configurations to generate
lattice:#
type: list | tuple
argument path: explore[calypso]/configurations[alloy]/lattice
The lattice. Should be a list providing [ “lattice_type”, lattice_const ], or a list providing [ “/path/to/dpdata/system”, “fmt” ]. The two styles are distinguished by the type of the second element. Currently “lattice_type” can be “bcc”, “fcc”, “hcp”, “sc” or “diamond”.
replicate:#
type: list | NoneType, optional, default: None
argument path: explore[calypso]/configurations[alloy]/replicate
The number of replicates in each direction
concentration:#
type: list | NoneType, optional, default: None
argument path: explore[calypso]/configurations[alloy]/concentration
The concentration of each element. List[List[float]] or List[float] or None. If List[float], the concentrations of each element. The length of the list should be the same as the type_map. If List[List[float]], a list of concentrations (List[float]) is randomly picked from the List. If None, the elements are assumed to be of equal concentration.
cell_pert_frac:#
type: float, optional, default: 0.0
argument path: explore[calypso]/configurations[alloy]/cell_pert_frac
The faction of cell perturbation
atom_pert_dist:#
type: float, optional, default: 0.0
argument path: explore[calypso]/configurations[alloy]/atom_pert_dist
The distance of atomic position perturbation
When type is set to file:
Generate alloys from user provided file(s). The file(s) are assume to be load by dpdata.
files:#
type: str | list
argument path: explore[calypso]/configurations[file]/files
The paths to the configuration files. widecards are supported.
prefix:#
type: str | NoneType, optional, default: None
argument path: explore[calypso]/configurations[file]/prefix
The prefix of file paths.
fmt:#
type: str, optional, default: auto
argument path: explore[calypso]/configurations[file]/fmt
The format (dpdata accepted formats) of the files.
remove_pbc:#
type: bool, optional, default: False
argument path: explore[calypso]/configurations[file]/remove_pbc
The remove the pbc of the data. shift the coords to the center of box so it can be used with lammps.
stages:#
type: typing.List[typing.List[dict]]
argument path: explore[calypso]/stages
The definition of exploration stages of type List[List[ExplorationTaskGroup]. The outer list provides the enumeration of the exploration stages. Then each stage is defined by a list of exploration task groups. Each task group is described in the task group definition
filters:#
type: list | dict, optional, default: []
argument path: explore[calypso]/filters
A list of configuration filters
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: explore[calypso]/filters/type
possible choices: distance, box_skewness, box_length
the type of the configuration filter.
distance: Configuration filter of type distance
box_skewness: Configuration filter of type box_skewness
box_length: Configuration filter of type box_length
When type is set to distance:
Configuration filter of type distance
max_workers:#
type: NoneType | int, optional, default: None
argument path: explore[calypso]/filters[distance]/max_workers
The maximum number of processes used to filter configurations, None represents as many as the processors of the machine, and 1 for serial
custom_safe_dist:#
type: dict, optional, default: {}
argument path: explore[calypso]/filters[distance]/custom_safe_dist
Custom safe distance (in unit of bohr) for each element
safe_dist_ratio:#
type: float, optional, default: 1.0
argument path: explore[calypso]/filters[distance]/safe_dist_ratio
The ratio multiplied to the safe distance
When type is set to box_skewness:
Configuration filter of type box_skewness
max_workers:#
type: NoneType | int, optional, default: None
argument path: explore[calypso]/filters[box_skewness]/max_workers
The maximum number of processes used to filter configurations, None represents as many as the processors of the machine, and 1 for serial
theta:#
type: float, optional, default: 60.0
argument path: explore[calypso]/filters[box_skewness]/theta
The threshold for angles between the edges of the cell. If all angles are larger than this value the check is passed
When type is set to box_length:
Configuration filter of type box_length
max_workers:#
type: NoneType | int, optional, default: None
argument path: explore[calypso]/filters[box_length]/max_workers
The maximum number of processes used to filter configurations, None represents as many as the processors of the machine, and 1 for serial
length_ratio:#
type: float, optional, default: 5.0
argument path: explore[calypso]/filters[box_length]/length_ratio
The threshold for the length ratio between the edges of the cell. If all length ratios are smaller than this value the check is passed
When type is set to calypso:default:
The exploration by CALYPSO structure prediction
config:#
type: dict, optional, default: {'command': 'lmp', 'teacher_model_path': None, 'shuffle_models': False, 'head': None, 'use_ele_temp': 0, 'model_frozen_head': None, 'use_hdf5': False, 'extra_output_files': []}
argument path: explore[calypso:default]/config
Configuration of calypso exploration
model_devi_group_size:#
type: int, optional
argument path: explore[calypso:default]/config/model_devi_group_size
group size for model deviation.
run_calypso_command:#
type: str, optional, default: calypso.x
argument path: explore[calypso:default]/config/run_calypso_command
command of running calypso.
run_opt_command:#
type: str, optional
argument path: explore[calypso:default]/config/run_opt_command
command of running optimization with dp.
max_numb_iter:#
type: int, optional, default: 10
argument path: explore[calypso:default]/max_numb_iter
Maximum number of iterations per stage
fatal_at_max:#
type: bool, optional, default: True
argument path: explore[calypso:default]/fatal_at_max
Fatal when the number of iteration per stage reaches the max_numb_iter
output_nopbc:#
type: bool, optional, default: False
argument path: explore[calypso:default]/output_nopbc
Remove pbc of the output configurations
convergence:#
type: dict
argument path: explore[calypso:default]/convergence
The method of convergence check.
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: explore[calypso:default]/convergence/type
possible choices: fixed-levels, fixed-levels-max-select, adaptive-lower
the type of the condidate selection and convergence check method.
fixed-levels: The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations will be randomly sampled from candidates for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
fixed-levels-max-select: The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations with maximal model deviation in the candidates are sent for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
adaptive-lower: The method of adaptive adjust the lower trust levels. In each step of iterations, a number (set by numb_candi_f or numb_candi_v) or a ratio (set by rate_candi_f or rate_candi_v) of configurations with a model deviation lower than the higher trust level (level_f_hi, level_v_hi) are treated as candidates. The lowest model deviation of the candidates are treated as the lower trust level. If the lower trust level does not change significant (controlled by conv_tolerance) in n_checked_steps, the stage is treated as converged.
When type is set to fixed-levels:
The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations will be randomly sampled from candidates for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
level_f_lo:#
type: float
argument path: explore[calypso:default]/convergence[fixed-levels]/level_f_lo
The lower trust level of force model deviation
level_f_hi:#
type: float
argument path: explore[calypso:default]/convergence[fixed-levels]/level_f_hi
The higher trust level of force model deviation
level_v_lo:#
type: NoneType | float, optional, default: None
argument path: explore[calypso:default]/convergence[fixed-levels]/level_v_lo
The lower trust level of virial model deviation
level_v_hi:#
type: NoneType | float, optional, default: None
argument path: explore[calypso:default]/convergence[fixed-levels]/level_v_hi
The higher trust level of virial model deviation
conv_accuracy:#
type: float, optional, default: 0.9
argument path: explore[calypso:default]/convergence[fixed-levels]/conv_accuracy
If the ratio of accurate frames is larger than this value, the stage is converged
When type is set to fixed-levels-max-select:
The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations with maximal model deviation in the candidates are sent for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
level_f_lo:#
type: float
argument path: explore[calypso:default]/convergence[fixed-levels-max-select]/level_f_lo
The lower trust level of force model deviation
level_f_hi:#
type: float
argument path: explore[calypso:default]/convergence[fixed-levels-max-select]/level_f_hi
The higher trust level of force model deviation
level_v_lo:#
type: NoneType | float, optional, default: None
argument path: explore[calypso:default]/convergence[fixed-levels-max-select]/level_v_lo
The lower trust level of virial model deviation
level_v_hi:#
type: NoneType | float, optional, default: None
argument path: explore[calypso:default]/convergence[fixed-levels-max-select]/level_v_hi
The higher trust level of virial model deviation
conv_accuracy:#
type: float, optional, default: 0.9
argument path: explore[calypso:default]/convergence[fixed-levels-max-select]/conv_accuracy
If the ratio of accurate frames is larger than this value, the stage is converged
When type is set to adaptive-lower:
The method of adaptive adjust the lower trust levels. In each step of iterations, a number (set by numb_candi_f or numb_candi_v) or a ratio (set by rate_candi_f or rate_candi_v) of configurations with a model deviation lower than the higher trust level (level_f_hi, level_v_hi) are treated as candidates. The lowest model deviation of the candidates are treated as the lower trust level. If the lower trust level does not change significant (controlled by conv_tolerance) in n_checked_steps, the stage is treated as converged.
level_f_hi:#
type: float, optional, default: 0.5
argument path: explore[calypso:default]/convergence[adaptive-lower]/level_f_hi
The higher trust level of force model deviation
numb_candi_f:#
type: int, optional, default: 200
argument path: explore[calypso:default]/convergence[adaptive-lower]/numb_candi_f
The number of force frames that has a model deviation lower than level_f_hi treated as candidate.
rate_candi_f:#
type: float, optional, default: 0.01
argument path: explore[calypso:default]/convergence[adaptive-lower]/rate_candi_f
The ratio of force frames that has a model deviation lower than level_f_hi treated as candidate.
level_v_hi:#
type: NoneType | float, optional, default: None
argument path: explore[calypso:default]/convergence[adaptive-lower]/level_v_hi
The higher trust level of virial model deviation
numb_candi_v:#
type: int, optional, default: 0
argument path: explore[calypso:default]/convergence[adaptive-lower]/numb_candi_v
The number of virial frames that has a model deviation lower than level_v_hi treated as candidate.
rate_candi_v:#
type: float, optional, default: 0.0
argument path: explore[calypso:default]/convergence[adaptive-lower]/rate_candi_v
The ratio of virial frames that has a model deviation lower than level_v_hi treated as candidate.
n_checked_steps:#
type: int, optional, default: 2
argument path: explore[calypso:default]/convergence[adaptive-lower]/n_checked_steps
The number of steps to check the convergence.
conv_tolerance:#
type: float, optional, default: 0.05
argument path: explore[calypso:default]/convergence[adaptive-lower]/conv_tolerance
The convergence tolerance.
candi_sel_prob:#
type: str, optional, default: uniform
argument path: explore[calypso:default]/convergence[adaptive-lower]/candi_sel_prob
The method for selecting candidates. It can be ‘uniform’: all candidates are of the same probability. ‘inv_pop_f’ or ‘inv_pop_f:nhist’: the probability is inversely propotional to the population of a histogram between leven_f_lo and level_f_hi. The number of bins in the histogram is set by nhist, which should be an integer. The default is 10.
configurations:#
type: list, alias: configuration
argument path: explore[calypso:default]/configurations
A list of initial configurations.
This argument takes a list with each element containing the following:
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: explore[calypso:default]/configurations/type
possible choices: alloy, file
the type of the initial configuration generator.
alloy: Generate alloys with a certain lattice or user proided structure, the elements randomly occuping the lattice with user provided probability .
file: Generate alloys from user provided file(s). The file(s) are assume to be load by dpdata.
When type is set to alloy:
Generate alloys with a certain lattice or user proided structure, the elements randomly occuping the lattice with user provided probability .
numb_confs:#
type: int, optional, default: 1
argument path: explore[calypso:default]/configurations[alloy]/numb_confs
The number of configurations to generate
lattice:#
type: list | tuple
argument path: explore[calypso:default]/configurations[alloy]/lattice
The lattice. Should be a list providing [ “lattice_type”, lattice_const ], or a list providing [ “/path/to/dpdata/system”, “fmt” ]. The two styles are distinguished by the type of the second element. Currently “lattice_type” can be “bcc”, “fcc”, “hcp”, “sc” or “diamond”.
replicate:#
type: list | NoneType, optional, default: None
argument path: explore[calypso:default]/configurations[alloy]/replicate
The number of replicates in each direction
concentration:#
type: list | NoneType, optional, default: None
argument path: explore[calypso:default]/configurations[alloy]/concentration
The concentration of each element. List[List[float]] or List[float] or None. If List[float], the concentrations of each element. The length of the list should be the same as the type_map. If List[List[float]], a list of concentrations (List[float]) is randomly picked from the List. If None, the elements are assumed to be of equal concentration.
cell_pert_frac:#
type: float, optional, default: 0.0
argument path: explore[calypso:default]/configurations[alloy]/cell_pert_frac
The faction of cell perturbation
atom_pert_dist:#
type: float, optional, default: 0.0
argument path: explore[calypso:default]/configurations[alloy]/atom_pert_dist
The distance of atomic position perturbation
When type is set to file:
Generate alloys from user provided file(s). The file(s) are assume to be load by dpdata.
files:#
type: str | list
argument path: explore[calypso:default]/configurations[file]/files
The paths to the configuration files. widecards are supported.
prefix:#
type: str | NoneType, optional, default: None
argument path: explore[calypso:default]/configurations[file]/prefix
The prefix of file paths.
fmt:#
type: str, optional, default: auto
argument path: explore[calypso:default]/configurations[file]/fmt
The format (dpdata accepted formats) of the files.
remove_pbc:#
type: bool, optional, default: False
argument path: explore[calypso:default]/configurations[file]/remove_pbc
The remove the pbc of the data. shift the coords to the center of box so it can be used with lammps.
stages:#
type: typing.List[typing.List[dict]]
argument path: explore[calypso:default]/stages
The definition of exploration stages of type List[List[ExplorationTaskGroup]. The outer list provides the enumeration of the exploration stages. Then each stage is defined by a list of exploration task groups. Each task group is described in the task group definition
filters:#
type: list | dict, optional, default: []
argument path: explore[calypso:default]/filters
A list of configuration filters
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: explore[calypso:default]/filters/type
possible choices: distance, box_skewness, box_length
the type of the configuration filter.
distance: Configuration filter of type distance
box_skewness: Configuration filter of type box_skewness
box_length: Configuration filter of type box_length
When type is set to distance:
Configuration filter of type distance
max_workers:#
type: NoneType | int, optional, default: None
argument path: explore[calypso:default]/filters[distance]/max_workers
The maximum number of processes used to filter configurations, None represents as many as the processors of the machine, and 1 for serial
custom_safe_dist:#
type: dict, optional, default: {}
argument path: explore[calypso:default]/filters[distance]/custom_safe_dist
Custom safe distance (in unit of bohr) for each element
safe_dist_ratio:#
type: float, optional, default: 1.0
argument path: explore[calypso:default]/filters[distance]/safe_dist_ratio
The ratio multiplied to the safe distance
When type is set to box_skewness:
Configuration filter of type box_skewness
max_workers:#
type: NoneType | int, optional, default: None
argument path: explore[calypso:default]/filters[box_skewness]/max_workers
The maximum number of processes used to filter configurations, None represents as many as the processors of the machine, and 1 for serial
theta:#
type: float, optional, default: 60.0
argument path: explore[calypso:default]/filters[box_skewness]/theta
The threshold for angles between the edges of the cell. If all angles are larger than this value the check is passed
When type is set to box_length:
Configuration filter of type box_length
max_workers:#
type: NoneType | int, optional, default: None
argument path: explore[calypso:default]/filters[box_length]/max_workers
The maximum number of processes used to filter configurations, None represents as many as the processors of the machine, and 1 for serial
length_ratio:#
type: float, optional, default: 5.0
argument path: explore[calypso:default]/filters[box_length]/length_ratio
The threshold for the length ratio between the edges of the cell. If all length ratios are smaller than this value the check is passed
When type is set to calypso:merge:
The exploration by CALYPSO structure prediction
config:#
type: dict, optional, default: {'command': 'lmp', 'teacher_model_path': None, 'shuffle_models': False, 'head': None, 'use_ele_temp': 0, 'model_frozen_head': None, 'use_hdf5': False, 'extra_output_files': []}
argument path: explore[calypso:merge]/config
Configuration of calypso exploration
model_devi_group_size:#
type: int, optional
argument path: explore[calypso:merge]/config/model_devi_group_size
group size for model deviation.
run_calypso_command:#
type: str, optional, default: calypso.x
argument path: explore[calypso:merge]/config/run_calypso_command
command of running calypso.
run_opt_command:#
type: str, optional
argument path: explore[calypso:merge]/config/run_opt_command
command of running optimization with dp.
max_numb_iter:#
type: int, optional, default: 10
argument path: explore[calypso:merge]/max_numb_iter
Maximum number of iterations per stage
fatal_at_max:#
type: bool, optional, default: True
argument path: explore[calypso:merge]/fatal_at_max
Fatal when the number of iteration per stage reaches the max_numb_iter
output_nopbc:#
type: bool, optional, default: False
argument path: explore[calypso:merge]/output_nopbc
Remove pbc of the output configurations
convergence:#
type: dict
argument path: explore[calypso:merge]/convergence
The method of convergence check.
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: explore[calypso:merge]/convergence/type
possible choices: fixed-levels, fixed-levels-max-select, adaptive-lower
the type of the condidate selection and convergence check method.
fixed-levels: The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations will be randomly sampled from candidates for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
fixed-levels-max-select: The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations with maximal model deviation in the candidates are sent for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
adaptive-lower: The method of adaptive adjust the lower trust levels. In each step of iterations, a number (set by numb_candi_f or numb_candi_v) or a ratio (set by rate_candi_f or rate_candi_v) of configurations with a model deviation lower than the higher trust level (level_f_hi, level_v_hi) are treated as candidates. The lowest model deviation of the candidates are treated as the lower trust level. If the lower trust level does not change significant (controlled by conv_tolerance) in n_checked_steps, the stage is treated as converged.
When type is set to fixed-levels:
The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations will be randomly sampled from candidates for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
level_f_lo:#
type: float
argument path: explore[calypso:merge]/convergence[fixed-levels]/level_f_lo
The lower trust level of force model deviation
level_f_hi:#
type: float
argument path: explore[calypso:merge]/convergence[fixed-levels]/level_f_hi
The higher trust level of force model deviation
level_v_lo:#
type: NoneType | float, optional, default: None
argument path: explore[calypso:merge]/convergence[fixed-levels]/level_v_lo
The lower trust level of virial model deviation
level_v_hi:#
type: NoneType | float, optional, default: None
argument path: explore[calypso:merge]/convergence[fixed-levels]/level_v_hi
The higher trust level of virial model deviation
conv_accuracy:#
type: float, optional, default: 0.9
argument path: explore[calypso:merge]/convergence[fixed-levels]/conv_accuracy
If the ratio of accurate frames is larger than this value, the stage is converged
When type is set to fixed-levels-max-select:
The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations with maximal model deviation in the candidates are sent for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
level_f_lo:#
type: float
argument path: explore[calypso:merge]/convergence[fixed-levels-max-select]/level_f_lo
The lower trust level of force model deviation
level_f_hi:#
type: float
argument path: explore[calypso:merge]/convergence[fixed-levels-max-select]/level_f_hi
The higher trust level of force model deviation
level_v_lo:#
type: NoneType | float, optional, default: None
argument path: explore[calypso:merge]/convergence[fixed-levels-max-select]/level_v_lo
The lower trust level of virial model deviation
level_v_hi:#
type: NoneType | float, optional, default: None
argument path: explore[calypso:merge]/convergence[fixed-levels-max-select]/level_v_hi
The higher trust level of virial model deviation
conv_accuracy:#
type: float, optional, default: 0.9
argument path: explore[calypso:merge]/convergence[fixed-levels-max-select]/conv_accuracy
If the ratio of accurate frames is larger than this value, the stage is converged
When type is set to adaptive-lower:
The method of adaptive adjust the lower trust levels. In each step of iterations, a number (set by numb_candi_f or numb_candi_v) or a ratio (set by rate_candi_f or rate_candi_v) of configurations with a model deviation lower than the higher trust level (level_f_hi, level_v_hi) are treated as candidates. The lowest model deviation of the candidates are treated as the lower trust level. If the lower trust level does not change significant (controlled by conv_tolerance) in n_checked_steps, the stage is treated as converged.
level_f_hi:#
type: float, optional, default: 0.5
argument path: explore[calypso:merge]/convergence[adaptive-lower]/level_f_hi
The higher trust level of force model deviation
numb_candi_f:#
type: int, optional, default: 200
argument path: explore[calypso:merge]/convergence[adaptive-lower]/numb_candi_f
The number of force frames that has a model deviation lower than level_f_hi treated as candidate.
rate_candi_f:#
type: float, optional, default: 0.01
argument path: explore[calypso:merge]/convergence[adaptive-lower]/rate_candi_f
The ratio of force frames that has a model deviation lower than level_f_hi treated as candidate.
level_v_hi:#
type: NoneType | float, optional, default: None
argument path: explore[calypso:merge]/convergence[adaptive-lower]/level_v_hi
The higher trust level of virial model deviation
numb_candi_v:#
type: int, optional, default: 0
argument path: explore[calypso:merge]/convergence[adaptive-lower]/numb_candi_v
The number of virial frames that has a model deviation lower than level_v_hi treated as candidate.
rate_candi_v:#
type: float, optional, default: 0.0
argument path: explore[calypso:merge]/convergence[adaptive-lower]/rate_candi_v
The ratio of virial frames that has a model deviation lower than level_v_hi treated as candidate.
n_checked_steps:#
type: int, optional, default: 2
argument path: explore[calypso:merge]/convergence[adaptive-lower]/n_checked_steps
The number of steps to check the convergence.
conv_tolerance:#
type: float, optional, default: 0.05
argument path: explore[calypso:merge]/convergence[adaptive-lower]/conv_tolerance
The convergence tolerance.
candi_sel_prob:#
type: str, optional, default: uniform
argument path: explore[calypso:merge]/convergence[adaptive-lower]/candi_sel_prob
The method for selecting candidates. It can be ‘uniform’: all candidates are of the same probability. ‘inv_pop_f’ or ‘inv_pop_f:nhist’: the probability is inversely propotional to the population of a histogram between leven_f_lo and level_f_hi. The number of bins in the histogram is set by nhist, which should be an integer. The default is 10.
configurations:#
type: list, alias: configuration
argument path: explore[calypso:merge]/configurations
A list of initial configurations.
This argument takes a list with each element containing the following:
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: explore[calypso:merge]/configurations/type
possible choices: alloy, file
the type of the initial configuration generator.
alloy: Generate alloys with a certain lattice or user proided structure, the elements randomly occuping the lattice with user provided probability .
file: Generate alloys from user provided file(s). The file(s) are assume to be load by dpdata.
When type is set to alloy:
Generate alloys with a certain lattice or user proided structure, the elements randomly occuping the lattice with user provided probability .
numb_confs:#
type: int, optional, default: 1
argument path: explore[calypso:merge]/configurations[alloy]/numb_confs
The number of configurations to generate
lattice:#
type: list | tuple
argument path: explore[calypso:merge]/configurations[alloy]/lattice
The lattice. Should be a list providing [ “lattice_type”, lattice_const ], or a list providing [ “/path/to/dpdata/system”, “fmt” ]. The two styles are distinguished by the type of the second element. Currently “lattice_type” can be “bcc”, “fcc”, “hcp”, “sc” or “diamond”.
replicate:#
type: list | NoneType, optional, default: None
argument path: explore[calypso:merge]/configurations[alloy]/replicate
The number of replicates in each direction
concentration:#
type: list | NoneType, optional, default: None
argument path: explore[calypso:merge]/configurations[alloy]/concentration
The concentration of each element. List[List[float]] or List[float] or None. If List[float], the concentrations of each element. The length of the list should be the same as the type_map. If List[List[float]], a list of concentrations (List[float]) is randomly picked from the List. If None, the elements are assumed to be of equal concentration.
cell_pert_frac:#
type: float, optional, default: 0.0
argument path: explore[calypso:merge]/configurations[alloy]/cell_pert_frac
The faction of cell perturbation
atom_pert_dist:#
type: float, optional, default: 0.0
argument path: explore[calypso:merge]/configurations[alloy]/atom_pert_dist
The distance of atomic position perturbation
When type is set to file:
Generate alloys from user provided file(s). The file(s) are assume to be load by dpdata.
files:#
type: str | list
argument path: explore[calypso:merge]/configurations[file]/files
The paths to the configuration files. widecards are supported.
prefix:#
type: str | NoneType, optional, default: None
argument path: explore[calypso:merge]/configurations[file]/prefix
The prefix of file paths.
fmt:#
type: str, optional, default: auto
argument path: explore[calypso:merge]/configurations[file]/fmt
The format (dpdata accepted formats) of the files.
remove_pbc:#
type: bool, optional, default: False
argument path: explore[calypso:merge]/configurations[file]/remove_pbc
The remove the pbc of the data. shift the coords to the center of box so it can be used with lammps.
stages:#
type: typing.List[typing.List[dict]]
argument path: explore[calypso:merge]/stages
The definition of exploration stages of type List[List[ExplorationTaskGroup]. The outer list provides the enumeration of the exploration stages. Then each stage is defined by a list of exploration task groups. Each task group is described in the task group definition
filters:#
type: list | dict, optional, default: []
argument path: explore[calypso:merge]/filters
A list of configuration filters
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: explore[calypso:merge]/filters/type
possible choices: distance, box_skewness, box_length
the type of the configuration filter.
distance: Configuration filter of type distance
box_skewness: Configuration filter of type box_skewness
box_length: Configuration filter of type box_length
When type is set to distance:
Configuration filter of type distance
max_workers:#
type: NoneType | int, optional, default: None
argument path: explore[calypso:merge]/filters[distance]/max_workers
The maximum number of processes used to filter configurations, None represents as many as the processors of the machine, and 1 for serial
custom_safe_dist:#
type: dict, optional, default: {}
argument path: explore[calypso:merge]/filters[distance]/custom_safe_dist
Custom safe distance (in unit of bohr) for each element
safe_dist_ratio:#
type: float, optional, default: 1.0
argument path: explore[calypso:merge]/filters[distance]/safe_dist_ratio
The ratio multiplied to the safe distance
When type is set to box_skewness:
Configuration filter of type box_skewness
max_workers:#
type: NoneType | int, optional, default: None
argument path: explore[calypso:merge]/filters[box_skewness]/max_workers
The maximum number of processes used to filter configurations, None represents as many as the processors of the machine, and 1 for serial
theta:#
type: float, optional, default: 60.0
argument path: explore[calypso:merge]/filters[box_skewness]/theta
The threshold for angles between the edges of the cell. If all angles are larger than this value the check is passed
When type is set to box_length:
Configuration filter of type box_length
max_workers:#
type: NoneType | int, optional, default: None
argument path: explore[calypso:merge]/filters[box_length]/max_workers
The maximum number of processes used to filter configurations, None represents as many as the processors of the machine, and 1 for serial
length_ratio:#
type: float, optional, default: 5.0
argument path: explore[calypso:merge]/filters[box_length]/length_ratio
The threshold for the length ratio between the edges of the cell. If all length ratios are smaller than this value the check is passed
When type is set to diffcsp:
The exploration by DiffCSP
config:#
type: dict
argument path: explore[diffcsp]/config
Configuration of DiffCSP exploration
gen_tasks:#
type: int, optional, default: 1
argument path: explore[diffcsp]/config/gen_tasks
Number of DiffCSP generation tasks
gen_command:#
type: str
argument path: explore[diffcsp]/config/gen_command
Command for DiffCSP generation
relax_group_size:#
type: int, optional, default: 100
argument path: explore[diffcsp]/config/relax_group_size
Group size for relaxation
use_hdf5:#
type: bool, optional, default: False
argument path: explore[diffcsp]/config/use_hdf5
Use HDF5 to store trajs and model_devis
max_numb_iter:#
type: int, optional, default: 10
argument path: explore[diffcsp]/max_numb_iter
Maximum number of iterations per stage
fatal_at_max:#
type: bool, optional, default: True
argument path: explore[diffcsp]/fatal_at_max
Fatal when the number of iteration per stage reaches the max_numb_iter
output_nopbc:#
type: bool, optional, default: False
argument path: explore[diffcsp]/output_nopbc
Remove pbc of the output configurations
convergence:#
type: dict
argument path: explore[diffcsp]/convergence
The method of convergence check.
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: explore[diffcsp]/convergence/type
possible choices: fixed-levels, fixed-levels-max-select, adaptive-lower
the type of the condidate selection and convergence check method.
fixed-levels: The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations will be randomly sampled from candidates for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
fixed-levels-max-select: The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations with maximal model deviation in the candidates are sent for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
adaptive-lower: The method of adaptive adjust the lower trust levels. In each step of iterations, a number (set by numb_candi_f or numb_candi_v) or a ratio (set by rate_candi_f or rate_candi_v) of configurations with a model deviation lower than the higher trust level (level_f_hi, level_v_hi) are treated as candidates. The lowest model deviation of the candidates are treated as the lower trust level. If the lower trust level does not change significant (controlled by conv_tolerance) in n_checked_steps, the stage is treated as converged.
When type is set to fixed-levels:
The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations will be randomly sampled from candidates for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
level_f_lo:#
type: float
argument path: explore[diffcsp]/convergence[fixed-levels]/level_f_lo
The lower trust level of force model deviation
level_f_hi:#
type: float
argument path: explore[diffcsp]/convergence[fixed-levels]/level_f_hi
The higher trust level of force model deviation
level_v_lo:#
type: NoneType | float, optional, default: None
argument path: explore[diffcsp]/convergence[fixed-levels]/level_v_lo
The lower trust level of virial model deviation
level_v_hi:#
type: NoneType | float, optional, default: None
argument path: explore[diffcsp]/convergence[fixed-levels]/level_v_hi
The higher trust level of virial model deviation
conv_accuracy:#
type: float, optional, default: 0.9
argument path: explore[diffcsp]/convergence[fixed-levels]/conv_accuracy
If the ratio of accurate frames is larger than this value, the stage is converged
When type is set to fixed-levels-max-select:
The configurations with force model deviation between level_f_lo, level_f_hi or virial model deviation between level_v_lo and level_v_hi are treated as candidates (The virial model deviation check is optional). The configurations with maximal model deviation in the candidates are sent for FP calculations. If the ratio of accurate (below level_f_lo and level_v_lo) is higher then conv_accuracy, the stage is treated as converged.
level_f_lo:#
type: float
argument path: explore[diffcsp]/convergence[fixed-levels-max-select]/level_f_lo
The lower trust level of force model deviation
level_f_hi:#
type: float
argument path: explore[diffcsp]/convergence[fixed-levels-max-select]/level_f_hi
The higher trust level of force model deviation
level_v_lo:#
type: NoneType | float, optional, default: None
argument path: explore[diffcsp]/convergence[fixed-levels-max-select]/level_v_lo
The lower trust level of virial model deviation
level_v_hi:#
type: NoneType | float, optional, default: None
argument path: explore[diffcsp]/convergence[fixed-levels-max-select]/level_v_hi
The higher trust level of virial model deviation
conv_accuracy:#
type: float, optional, default: 0.9
argument path: explore[diffcsp]/convergence[fixed-levels-max-select]/conv_accuracy
If the ratio of accurate frames is larger than this value, the stage is converged
When type is set to adaptive-lower:
The method of adaptive adjust the lower trust levels. In each step of iterations, a number (set by numb_candi_f or numb_candi_v) or a ratio (set by rate_candi_f or rate_candi_v) of configurations with a model deviation lower than the higher trust level (level_f_hi, level_v_hi) are treated as candidates. The lowest model deviation of the candidates are treated as the lower trust level. If the lower trust level does not change significant (controlled by conv_tolerance) in n_checked_steps, the stage is treated as converged.
level_f_hi:#
type: float, optional, default: 0.5
argument path: explore[diffcsp]/convergence[adaptive-lower]/level_f_hi
The higher trust level of force model deviation
numb_candi_f:#
type: int, optional, default: 200
argument path: explore[diffcsp]/convergence[adaptive-lower]/numb_candi_f
The number of force frames that has a model deviation lower than level_f_hi treated as candidate.
rate_candi_f:#
type: float, optional, default: 0.01
argument path: explore[diffcsp]/convergence[adaptive-lower]/rate_candi_f
The ratio of force frames that has a model deviation lower than level_f_hi treated as candidate.
level_v_hi:#
type: NoneType | float, optional, default: None
argument path: explore[diffcsp]/convergence[adaptive-lower]/level_v_hi
The higher trust level of virial model deviation
numb_candi_v:#
type: int, optional, default: 0
argument path: explore[diffcsp]/convergence[adaptive-lower]/numb_candi_v
The number of virial frames that has a model deviation lower than level_v_hi treated as candidate.
rate_candi_v:#
type: float, optional, default: 0.0
argument path: explore[diffcsp]/convergence[adaptive-lower]/rate_candi_v
The ratio of virial frames that has a model deviation lower than level_v_hi treated as candidate.
n_checked_steps:#
type: int, optional, default: 2
argument path: explore[diffcsp]/convergence[adaptive-lower]/n_checked_steps
The number of steps to check the convergence.
conv_tolerance:#
type: float, optional, default: 0.05
argument path: explore[diffcsp]/convergence[adaptive-lower]/conv_tolerance
The convergence tolerance.
candi_sel_prob:#
type: str, optional, default: uniform
argument path: explore[diffcsp]/convergence[adaptive-lower]/candi_sel_prob
The method for selecting candidates. It can be ‘uniform’: all candidates are of the same probability. ‘inv_pop_f’ or ‘inv_pop_f:nhist’: the probability is inversely propotional to the population of a histogram between leven_f_lo and level_f_hi. The number of bins in the histogram is set by nhist, which should be an integer. The default is 10.
stages:#
type: typing.List[typing.List[dict]]
argument path: explore[diffcsp]/stages
The definition of exploration stages of type List[List[ExplorationTaskGroup]. The outer list provides the enumeration of the exploration stages. Then each stage is defined by a list of exploration task groups. Each task group is described in the task group definition
filters:#
type: list | dict, optional, default: []
argument path: explore[diffcsp]/filters
A list of configuration filters
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: explore[diffcsp]/filters/type
possible choices: distance, box_skewness, box_length
the type of the configuration filter.
distance: Configuration filter of type distance
box_skewness: Configuration filter of type box_skewness
box_length: Configuration filter of type box_length
When type is set to distance:
Configuration filter of type distance
max_workers:#
type: NoneType | int, optional, default: None
argument path: explore[diffcsp]/filters[distance]/max_workers
The maximum number of processes used to filter configurations, None represents as many as the processors of the machine, and 1 for serial
custom_safe_dist:#
type: dict, optional, default: {}
argument path: explore[diffcsp]/filters[distance]/custom_safe_dist
Custom safe distance (in unit of bohr) for each element
safe_dist_ratio:#
type: float, optional, default: 1.0
argument path: explore[diffcsp]/filters[distance]/safe_dist_ratio
The ratio multiplied to the safe distance
When type is set to box_skewness:
Configuration filter of type box_skewness
max_workers:#
type: NoneType | int, optional, default: None
argument path: explore[diffcsp]/filters[box_skewness]/max_workers
The maximum number of processes used to filter configurations, None represents as many as the processors of the machine, and 1 for serial
theta:#
type: float, optional, default: 60.0
argument path: explore[diffcsp]/filters[box_skewness]/theta
The threshold for angles between the edges of the cell. If all angles are larger than this value the check is passed
When type is set to box_length:
Configuration filter of type box_length
max_workers:#
type: NoneType | int, optional, default: None
argument path: explore[diffcsp]/filters[box_length]/max_workers
The maximum number of processes used to filter configurations, None represents as many as the processors of the machine, and 1 for serial
length_ratio:#
type: float, optional, default: 5.0
argument path: explore[diffcsp]/filters[box_length]/length_ratio
The threshold for the length ratio between the edges of the cell. If all length ratios are smaller than this value the check is passed

fp:#

type: dict
argument path: fp
The configuration for FP
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: fp/type
possible choices: vasp, gaussian, deepmd, fpop_abacus, fpop_cp2k
the type of the fp
When type is set to vasp:
inputs_config:#
type: dict
argument path: fp[vasp]/inputs_config
Configuration for preparing vasp inputs
incar:#
type: str
argument path: fp[vasp]/inputs_config/incar
The path to the template incar file
pp_files:#
type: dict
argument path: fp[vasp]/inputs_config/pp_files
The pseudopotential files set by a dict, e.g. {“Al” : “path/to/the/al/pp/file”, “Mg” : “path/to/the/mg/pp/file”}
kspacing:#
type: float
argument path: fp[vasp]/inputs_config/kspacing
The spacing of k-point sampling. ksapcing will overwrite the incar template
kgamma:#
type: bool, optional, default: True
argument path: fp[vasp]/inputs_config/kgamma
If the k-mesh includes the gamma point. kgamma will overwrite the incar template
run_config:#
type: dict
argument path: fp[vasp]/run_config
Configuration for running vasp tasks
command:#
type: str, optional, default: vasp
argument path: fp[vasp]/run_config/command
The command of VASP
out:#
type: str, optional, default: data
argument path: fp[vasp]/run_config/out
The output dir name of labeled data. In deepmd/npy format provided by dpdata.
log:#
type: str, optional, default: fp.log
argument path: fp[vasp]/run_config/log
The log file name of VASP
task_max:#
type: int, optional, default: 10
argument path: fp[vasp]/task_max
Maximum number of vasp tasks for each iteration
extra_output_files:#
type: list, optional, default: []
argument path: fp[vasp]/extra_output_files
Extra output file names, support wildcards
When type is set to gaussian:
inputs_config:#
type: dict
argument path: fp[gaussian]/inputs_config
Configuration for preparing vasp inputs
keywords:#
type: str | list
argument path: fp[gaussian]/inputs_config/keywords
Gaussian keywords, e.g. force b3lyp/6-31g**. If a list, run multiple steps.
multiplicity:#
type: str | int, optional, default: auto
argument path: fp[gaussian]/inputs_config/multiplicity
spin multiplicity state. It can be a number. If auto, multiplicity will be detected automatically, with the following rules:
fragment_guesses=True multiplicity will +1 for each radical, and +2 for each oxygen molecule
fragment_guesses=False multiplicity will be 1 or 2, but +2 for each oxygen molecule.
charge:#
type: int, optional, default: 0
argument path: fp[gaussian]/inputs_config/charge
molecule charge. Only used when charge is not provided by the system
basis_set:#
type: str, optional
argument path: fp[gaussian]/inputs_config/basis_set
custom basis set
keywords_high_multiplicity:#
type: str, optional
argument path: fp[gaussian]/inputs_config/keywords_high_multiplicity
keywords for points with multiple raicals. multiplicity should be auto. If not set, fallback to normal keywords
fragment_guesses:#
type: bool, optional, default: False
argument path: fp[gaussian]/inputs_config/fragment_guesses
initial guess generated from fragment guesses. If True, multiplicity should be auto
nproc:#
type: int, optional, default: 1
argument path: fp[gaussian]/inputs_config/nproc
Number of CPUs to use
run_config:#
type: dict
argument path: fp[gaussian]/run_config
Configuration for running vasp tasks
command:#
type: str, optional, default: g16
argument path: fp[gaussian]/run_config/command
The command of Gaussian
out:#
type: str, optional, default: data
argument path: fp[gaussian]/run_config/out
The output dir name of labeled data. In deepmd/npy format provided by dpdata.
post_command:#
type: str | NoneType, optional, default: None
argument path: fp[gaussian]/run_config/post_command
The command after Gaussian
task_max:#
type: int, optional, default: 10
argument path: fp[gaussian]/task_max
Maximum number of vasp tasks for each iteration
extra_output_files:#
type: list, optional, default: []
argument path: fp[gaussian]/extra_output_files
Extra output file names, support wildcards
When type is set to deepmd:
inputs_config:#
type: dict
argument path: fp[deepmd]/inputs_config
Configuration for preparing vasp inputs
run_config:#
type: dict
argument path: fp[deepmd]/run_config
Configuration for running vasp tasks
teacher_model_path:#
type: str | BinaryFileInput
argument path: fp[deepmd]/run_config/teacher_model_path
The path of teacher model, which can be loaded by deepmd.infer.DeepPot
out:#
type: str, optional, default: data
argument path: fp[deepmd]/run_config/out
The output dir name of labeled data. In deepmd/npy format provided by dpdata.
log:#
type: str, optional, default: fp.log
argument path: fp[deepmd]/run_config/log
The log file name of dp
task_max:#
type: int, optional, default: 10
argument path: fp[deepmd]/task_max
Maximum number of vasp tasks for each iteration
extra_output_files:#
type: list, optional, default: []
argument path: fp[deepmd]/extra_output_files
Extra output file names, support wildcards
When type is set to fpop_abacus:
inputs_config:#
type: dict
argument path: fp[fpop_abacus]/inputs_config
Configuration for preparing vasp inputs
input_file:#
type: str
argument path: fp[fpop_abacus]/inputs_config/input_file
A template INPUT file.
pp_files:#
type: dict
argument path: fp[fpop_abacus]/inputs_config/pp_files
The pseudopotential files for the elements. For example: {“H”: “/path/to/H.upf”, “O”: “/path/to/O.upf”}.
element_mass:#
type: dict | NoneType, optional, default: None
argument path: fp[fpop_abacus]/inputs_config/element_mass
Specify the mass of some elements. For example: {“H”: 1.0079, “O”: 15.9994}.
kpt_file:#
type: str | NoneType, optional, default: None
argument path: fp[fpop_abacus]/inputs_config/kpt_file
The KPT file, by default None.
orb_files:#
type: dict | NoneType, optional, default: None
argument path: fp[fpop_abacus]/inputs_config/orb_files
The numerical orbital fiels for the elements, by default None. For example: {“H”: “/path/to/H.orb”, “O”: “/path/to/O.orb”}.
deepks_descriptor:#
type: str | NoneType, optional, default: None
argument path: fp[fpop_abacus]/inputs_config/deepks_descriptor
The deepks descriptor file, by default None.
deepks_model:#
type: str | NoneType, optional, default: None
argument path: fp[fpop_abacus]/inputs_config/deepks_model
The deepks model file, by default None.
run_config:#
type: dict
argument path: fp[fpop_abacus]/run_config
Configuration for running vasp tasks
command:#
type: str, optional, default: abacus
argument path: fp[fpop_abacus]/run_config/command
The command of abacus
task_max:#
type: int, optional, default: 10
argument path: fp[fpop_abacus]/task_max
Maximum number of vasp tasks for each iteration
extra_output_files:#
type: list, optional, default: []
argument path: fp[fpop_abacus]/extra_output_files
Extra output file names, support wildcards
When type is set to fpop_cp2k:
inputs_config:#
type: dict
argument path: fp[fpop_cp2k]/inputs_config
Configuration for preparing vasp inputs
inp_file:#
type: str
argument path: fp[fpop_cp2k]/inputs_config/inp_file
The path to the user-submitted CP2K input file.
run_config:#
type: dict
argument path: fp[fpop_cp2k]/run_config
Configuration for running vasp tasks
command:#
type: str, optional, default: cp2k
argument path: fp[fpop_cp2k]/run_config/command
The command of cp2k
task_max:#
type: int, optional, default: 10
argument path: fp[fpop_cp2k]/task_max
Maximum number of vasp tasks for each iteration
extra_output_files:#
type: list, optional, default: []
argument path: fp[fpop_cp2k]/extra_output_files
Extra output file names, support wildcards

name:#: type: str, optional, default: dpgen
argument path: name
The workflow name, ‘dpgen’ for default

parallelism:#: type: NoneType | int, optional, default: None
argument path: parallelism
The parallelism for the workflow. Accept an int that stands for the maximum number of running pods for the workflow. None for default

Task group definition#

LAMMPS task group#

task_group:#

type: dict
argument path: task_group
Depending on the value of type, different sub args are accepted.
type:#
type: str (flag key)
argument path: task_group/type
possible choices: lmp-md, lmp-template, customized-lmp-template
the type of the task group
lmp-md: Lammps MD tasks. DPGEN will generate the lammps input script
lmp-template: Lammps MD tasks defined by templates. User provide lammps (and plumed) template for lammps tasks. The variables in templates are revised by the revisions key. Notice that the lines for pair style, dump and plumed are reserved for the revision of dpgen2, and the users should not write these lines by themselves. Rather, users notify dpgen2 the poistion of the line for pair_style by writting ‘pair_style deepmd’, the line for dump by writting ‘dump dpgen_dump’. If plumed is used, the line for fix plumed shouldbe written exactly as ‘fix dpgen_plm’.
customized-lmp-template: Lammps MD tasks defined by user customized shell commands and templates. User provided shell script generates a series of folders, and each folder contains a lammps template task group.
When type is set to lmp-md (or its alias lmp-npt):
Lammps MD tasks. DPGEN will generate the lammps input script
conf_idx:#
type: list, alias: sys_idx
argument path: task_group[lmp-md]/conf_idx
The configurations of configurations[conf_idx] will be used to generate the initial configurations of the tasks. This key provides the index of selected item in the configurations array.
n_sample:#
type: NoneType | int, optional, default: None
argument path: task_group[lmp-md]/n_sample
Number of configurations. If this number is smaller than the number of configruations in configruations[conf_idx], then n_sample configruations are randomly sampled from configruations[conf_idx], otherwise all configruations in configruations[conf_idx] will be used. If not provided, all configruations in configruations[conf_idx] will be used.
temps:#
type: list, alias: Ts
argument path: task_group[lmp-md]/temps
A list of temperatures in K. Also used to initialize the temperature
press:#
type: list, optional, alias: Ps
argument path: task_group[lmp-md]/press
A list of pressures in bar.
ens:#
type: str, optional, default: nve, alias: ensemble
argument path: task_group[lmp-md]/ens
The ensemble. Allowd options are ‘nve’, ‘nvt’, ‘npt’, ‘npt-a’, ‘npt-t’. ‘npt-a’ stands for anisotrpic box sampling and ‘npt-t’ stands for triclinic box sampling.
dt:#
type: float, optional, default: 0.001
argument path: task_group[lmp-md]/dt
The time step
nsteps:#
type: int, optional, default: 100
argument path: task_group[lmp-md]/nsteps
The number of steps
trj_freq:#
type: int, optional, default: 10, aliases: t_freq, trj_freq, traj_freq
argument path: task_group[lmp-md]/trj_freq
The frequency of dumping configurations and thermodynamic states
tau_t:#
type: float, optional, default: 0.05
argument path: task_group[lmp-md]/tau_t
The time scale of thermostat
tau_p:#
type: float, optional, default: 0.5
argument path: task_group[lmp-md]/tau_p
The time scale of barostat
pka_e:#
type: NoneType | float, optional, default: None
argument path: task_group[lmp-md]/pka_e
The energy of primary knock-on atom
neidelay:#
type: NoneType | int, optional, default: None
argument path: task_group[lmp-md]/neidelay
The delay of updating the neighbor list
no_pbc:#
type: bool, optional, default: False
argument path: task_group[lmp-md]/no_pbc
Not using the periodic boundary condition
use_clusters:#
type: bool, optional, default: False
argument path: task_group[lmp-md]/use_clusters
Calculate atomic model deviation
relative_f_epsilon:#
type: NoneType | float, optional, default: None
argument path: task_group[lmp-md]/relative_f_epsilon
Calculate relative force model deviation
relative_v_epsilon:#
type: NoneType | float, optional, default: None
argument path: task_group[lmp-md]/relative_v_epsilon
Calculate relative virial model deviation
ele_temp_f:#
type: NoneType | float, optional, default: None
argument path: task_group[lmp-md]/ele_temp_f
The electron temperature set by frame style
ele_temp_a:#
type: NoneType | float, optional, default: None
argument path: task_group[lmp-md]/ele_temp_a
The electron temperature set by atomistic style
pimd_bead:#
type: str | NoneType, optional, default: None
argument path: task_group[lmp-md]/pimd_bead
Bead index for PIMD, None for non-PIMD
input_extra_files:#
type: list, optional, default: []
argument path: task_group[lmp-md]/input_extra_files
Extra files that may be needed during exploration (e.g., ZBL parameter files)
When type is set to lmp-template:
Lammps MD tasks defined by templates. User provide lammps (and plumed) template for lammps tasks. The variables in templates are revised by the revisions key. Notice that the lines for pair style, dump and plumed are reserved for the revision of dpgen2, and the users should not write these lines by themselves. Rather, users notify dpgen2 the poistion of the line for pair_style by writting ‘pair_style deepmd’, the line for dump by writting ‘dump dpgen_dump’. If plumed is used, the line for fix plumed shouldbe written exactly as ‘fix dpgen_plm’.
conf_idx:#
type: list, alias: sys_idx
argument path: task_group[lmp-template]/conf_idx
The configurations of configurations[conf_idx] will be used to generate the initial configurations of the tasks. This key provides the index of selected item in the configurations array.
n_sample:#
type: NoneType | int, optional, default: None
argument path: task_group[lmp-template]/n_sample
Number of configurations. If this number is smaller than the number of configruations in configruations[conf_idx], then n_sample configruations are randomly sampled from configruations[conf_idx], otherwise all configruations in configruations[conf_idx] will be used. If not provided, all configruations in configruations[conf_idx] will be used.
lmp_template_fname:#
type: str, aliases: lmp_template, lmp
argument path: task_group[lmp-template]/lmp_template_fname
The file name of lammps input template
plm_template_fname:#
type: str | NoneType, optional, default: None, aliases: plm_template, plm
argument path: task_group[lmp-template]/plm_template_fname
The file name of plumed input template
revisions:#
type: dict, optional, default: {}
argument path: task_group[lmp-template]/revisions
The revisions. Should be a dict providing the key - list of desired values pair. Key is the word to be replaced in the templates, and it may appear in both the lammps and plumed input templates. All values in the value list will be enmerated.
traj_freq:#
type: int, optional, default: 10, aliases: t_freq, trj_freq, trj_freq
argument path: task_group[lmp-template]/traj_freq
The frequency of dumping configurations and thermodynamic states
extra_pair_style_args:#
type: str, optional, default: (empty string)
argument path: task_group[lmp-template]/extra_pair_style_args
The extra arguments for pair_style
pimd_bead:#
type: str | NoneType, optional, default: None
argument path: task_group[lmp-template]/pimd_bead
Bead index for PIMD, None for non-PIMD
input_extra_files:#
type: list, optional, default: []
argument path: task_group[lmp-template]/input_extra_files
Extra files that may be needed during exploration (e.g., ZBL parameter files)
When type is set to customized-lmp-template:
Lammps MD tasks defined by user customized shell commands and templates. User provided shell script generates a series of folders, and each folder contains a lammps template task group.
conf_idx:#
type: list, alias: sys_idx
argument path: task_group[customized-lmp-template]/conf_idx
The configurations of configurations[conf_idx] will be used to generate the initial configurations of the tasks. This key provides the index of selected item in the configurations array.
n_sample:#
type: NoneType | int, optional, default: None
argument path: task_group[customized-lmp-template]/n_sample
Number of configurations. If this number is smaller than the number of configruations in configruations[conf_idx], then n_sample configruations are randomly sampled from configruations[conf_idx], otherwise all configruations in configruations[conf_idx] will be used. If not provided, all configruations in configruations[conf_idx] will be used.
custom_shell_commands:#
type: list
argument path: task_group[customized-lmp-template]/custom_shell_commands
Customized shell commands to be run for each configuration. The commands require input_lmp_conf_name as input conf file, input_lmp_tmpl_name and input_plm_tmpl_name as templates, and input_extra_files as extra input files. By running the commands a series folders in pattern output_dir_pattern are supposed to be generated, and each folder is supposed to contain a configuration file output_lmp_conf_name, a lammps template file output_lmp_tmpl_name and a plumed template file output_plm_tmpl_name.
revisions:#
type: dict, optional, default: {}
argument path: task_group[customized-lmp-template]/revisions
The revisions. Should be a dict providing the key - list of desired values pair. Key is the word to be replaced in the templates, and it may appear in both the lammps and plumed input templates. All values in the value list will be enmerated.
traj_freq:#
type: int, optional, default: 10, aliases: t_freq, trj_freq, trj_freq
argument path: task_group[customized-lmp-template]/traj_freq
The frequency of dumping configurations and thermodynamic states
input_lmp_conf_name:#
type: str, optional, default: conf.lmp
argument path: task_group[customized-lmp-template]/input_lmp_conf_name
Input conf file name for the shell commands.
input_lmp_tmpl_name:#
type: str, optional, default: in.lammps, aliases: lmp_template, lmp
argument path: task_group[customized-lmp-template]/input_lmp_tmpl_name
The file name of lammps input template
input_plm_tmpl_name:#
type: str | NoneType, optional, default: None, aliases: plm_template, plm
argument path: task_group[customized-lmp-template]/input_plm_tmpl_name
The file name of plumed input template
input_extra_files:#
type: list, optional, default: []
argument path: task_group[customized-lmp-template]/input_extra_files
Extra files that may be needed to execute the shell commands
output_dir_pattern:#
type: str | list, optional, default: *
argument path: task_group[customized-lmp-template]/output_dir_pattern
Pattern of resultant folders generated by the shell commands.
output_lmp_conf_name:#
type: str, optional, default: conf.lmp
argument path: task_group[customized-lmp-template]/output_lmp_conf_name
Generated conf file name.
output_lmp_tmpl_name:#
type: str, optional, default: in.lammps
argument path: task_group[customized-lmp-template]/output_lmp_tmpl_name
Generated lmp input file name.
output_plm_tmpl_name:#
type: str, optional, default: input.plumed
argument path: task_group[customized-lmp-template]/output_plm_tmpl_name
Generated plm input file name.

CALYPSO task group#

task_group:

type: dict
argument path: task_group
CALYPSO structure prediction tasks. DPGEN will generate the calypso input script
numb_of_species:#
type: int
argument path: task_group/numb_of_species
number of species.
name_of_atoms:#
type: list
argument path: task_group/name_of_atoms
name of atoms.
atomic_number:#
type: list, optional
argument path: task_group/atomic_number
atomic number of each element.
numb_of_atoms:#
type: list
argument path: task_group/numb_of_atoms
number of each atom.
distance_of_ions:#
type: list | dict, optional
argument path: task_group/distance_of_ions
the distance matrix between different elements.
pop_size:#
type: int, optional, default: 30
argument path: task_group/pop_size
the number of structures would be generated in each step.
max_step:#
type: int, optional, default: 5
argument path: task_group/max_step
the max iteration number of CALYPSO loop.
system_name:#
type: str, optional, default: CALYPSO
argument path: task_group/system_name
system name.
numb_of_formula:#
type: list, optional, default: [1, 1]
argument path: task_group/numb_of_formula
the formula range of simulation cell.
pressure:#
type: float, optional, default: 0.001
argument path: task_group/pressure
the aim pressure (in Kbar) when using MLP to optimize structures.
fmax:#
type: float, optional, default: 0.01
argument path: task_group/fmax
the converge criterion. The force on all individual atoms should be less than fmax.
opt_step:#
type: float, optional, default: 1000
argument path: task_group/opt_step
the converge criterion. The force on all individual atoms should be less than fmax.
volume:#
type: float, optional, default: 0
argument path: task_group/volume
the volume of simulation cell in one formula.
ialgo:#
type: int, optional, default: 2
argument path: task_group/ialgo
the evolution algorithm of CALYPSO. 1: global pso, 2: local pso, 3: sabc.
pso_ratio:#
type: float, optional, default: 0.6
argument path: task_group/pso_ratio
the ratio of structures generated by evolution algorithm in one step.
icode:#
type: int, optional, default: 15
argument path: task_group/icode
the software of structure optimization. 1: VASP, 15: DP.
numb_of_lbest:#
type: int, optional, default: 4
argument path: task_group/numb_of_lbest
the number of evolution direction when using LPSO.
numb_of_local_optim:#
type: int, optional, default: 3
argument path: task_group/numb_of_local_optim
the number of making structure optimization when using dft.
command:#
type: str, optional, default: sh submit.sh
argument path: task_group/command
the command of running structure optimization.
max_time:#
type: int, optional, default: 9000
argument path: task_group/max_time
the max time (in second) of structure optimization.
pick_up:#
type: bool, optional, default: False
argument path: task_group/pick_up
whether to continue the calculation.
pick_step:#
type: int, optional, default: 0
argument path: task_group/pick_step
from which step to continue the calculation.
parallel:#
type: bool, optional, default: False
argument path: task_group/parallel
whether to run calypso in parallel.
split:#
type: bool, optional, default: True
argument path: task_group/split
sperate generating structures and structure optimizations. in dpgen2, split must be True.
spec_space_group:#
type: list, optional, default: [2, 230]
argument path: task_group/spec_space_group
the range of spacegroup.
vsc:#
type: bool, optional, default: False
argument path: task_group/vsc
whether to run calypso in variational stoichiometry way.
ctrl_range:#
type: list, optional, default: [[1, 10]]
argument path: task_group/ctrl_range
the atom range of each atoms.
max_numb_atoms:#
type: int, optional, default: 100
argument path: task_group/max_numb_atoms
the max number of atoms.

Arguments of the submit script

Contents

Arguments of the submit script#

Task group definition#

LAMMPS task group#

CALYPSO task group#