deepmd.model package

class deepmd.model.DipoleModel(*args, **kwargs)[source]

Bases: TensorModel

Methods

`build`(coord_, atype_, natoms, box, mesh, ...)	Build the model.
`build_descrpt`(coord_, atype_, natoms, box, ...)	Build the descriptor part of the model.
`init_variables`(graph, graph_def[, ...])	Init the embedding net variables with the given frozen model.

data_stat
get_ntypes
get_out_size
get_rcut
get_sel_type
get_type_map

class deepmd.model.EnerModel(descrpt, fitting, typeebd=None, type_map: List[str] | None = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01, use_srtab: str | None = None, smin_alpha: float | None = None, sw_rmin: float | None = None, sw_rmax: float | None = None)[source]

Bases: Model

Energy model.

Parameters:

descrpt: Descriptor
fitting: Fitting net
type_map: Mapping atom type to the name (str) of the type. For example type_map[1] gives the name of the type 1.
data_stat_nbatch: Number of frames used for data statistic
data_stat_protect: Protect parameter for atomic energy regression
use_srtab: The table for the short-range pairwise interaction added on top of DP. The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. The first colume is the distance between atoms. The second to the last columes are energies for pairs of certain types. For example we have two atom types, 0 and 1. The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly.
smin_alpha: The short-range tabulated interaction will be swithed according to the distance of the nearest neighbor. This distance is calculated by softmin. This parameter is the decaying parameter in the softmin. It is only required when use_srtab is provided.
sw_rmin: The lower boundary of the interpolation between short-range tabulated interaction and DP. It is only required when use_srtab is provided.
sw_rmin: The upper boundary of the interpolation between short-range tabulated interaction and DP. It is only required when use_srtab is provided.

Methods

`build`(coord_, atype_, natoms, box, mesh, ...)	Build the model.
`build_descrpt`(coord_, atype_, natoms, box, ...)	Build the descriptor part of the model.
`init_variables`(graph, graph_def[, ...])	Init the embedding net variables with the given frozen model.

data_stat
get_ntypes
get_rcut
get_type_map

build(coord_, atype_, natoms, box, mesh, input_dict, frz_model=None, ckpt_meta: str | None = None, suffix='', reuse=None)[source]

Build the model.

Parameters:

coord_tf.Tensor: The coordinates of atoms
atype_tf.Tensor: The atom types of atoms
natomstf.Tensor: The number of atoms
boxtf.Tensor: The box vectors
meshtf.Tensor: The mesh vectors
input_dictdict: The input dict
frz_modelstr, optional: The path to the frozen model
ckpt_metastr, optional: The path to the checkpoint and meta file
suffixstr, optional: The suffix of the scope
reusebool or tf.AUTO_REUSE, optional: Whether to reuse the variables

Returns:

dict: The output dict

data_stat(data)[source]

get_ntypes()[source]

get_rcut()[source]

get_type_map()[source]

init_variables(graph: Graph, graph_def: GraphDef, model_type: str = 'original_model', suffix: str = '') → None[source]

Init the embedding net variables with the given frozen model.

Parameters:

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
model_typestr: the type of the model
suffixstr: suffix to name scope

model_type = 'ener'

class deepmd.model.GlobalPolarModel(*args, **kwargs)[source]

Bases: TensorModel

Methods

`build`(coord_, atype_, natoms, box, mesh, ...)	Build the model.
`build_descrpt`(coord_, atype_, natoms, box, ...)	Build the descriptor part of the model.
`init_variables`(graph, graph_def[, ...])	Init the embedding net variables with the given frozen model.

data_stat
get_ntypes
get_out_size
get_rcut
get_sel_type
get_type_map

class deepmd.model.MultiModel(descrpt, fitting_dict, fitting_type_dict, typeebd=None, type_map: List[str] | None = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01, use_srtab: str | None = None, smin_alpha: float | None = None, sw_rmin: float | None = None, sw_rmax: float | None = None)[source]

Bases: Model

Multi-task model.

Parameters:

descrpt: Descriptor
fitting_dict: Dictionary of fitting nets
fitting_type_dict: Dictionary of types of fitting nets
typeebd: Type embedding net
type_map: Mapping atom type to the name (str) of the type. For example type_map[1] gives the name of the type 1.
data_stat_nbatch: Number of frames used for data statistic
data_stat_protect: Protect parameter for atomic energy regression
use_srtab: The table for the short-range pairwise interaction added on top of DP. The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. The first colume is the distance between atoms. The second to the last columes are energies for pairs of certain types. For example we have two atom types, 0 and 1. The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly.
smin_alpha: The short-range tabulated interaction will be swithed according to the distance of the nearest neighbor. This distance is calculated by softmin. This parameter is the decaying parameter in the softmin. It is only required when use_srtab is provided.
sw_rmin: The lower boundary of the interpolation between short-range tabulated interaction and DP. It is only required when use_srtab is provided.
sw_rmin: The upper boundary of the interpolation between short-range tabulated interaction and DP. It is only required when use_srtab is provided.

Methods

`build`(coord_, atype_, natoms, box, mesh, ...)	Build the model.
`build_descrpt`(coord_, atype_, natoms, box, ...)	Build the descriptor part of the model.
`init_variables`(graph, graph_def[, ...])	Init the embedding net variables with the given frozen model.

data_stat
get_ntypes
get_rcut
get_type_map

build(coord_, atype_, natoms, box, mesh, input_dict, frz_model=None, ckpt_meta: str | None = None, suffix='', reuse=None)[source]

Build the model.

Parameters:

coord_tf.Tensor: The coordinates of atoms
atype_tf.Tensor: The atom types of atoms
natomstf.Tensor: The number of atoms
boxtf.Tensor: The box vectors
meshtf.Tensor: The mesh vectors
input_dictdict: The input dict
frz_modelstr, optional: The path to the frozen model
ckpt_metastr, optional: The path to the checkpoint and meta file
suffixstr, optional: The suffix of the scope
reusebool or tf.AUTO_REUSE, optional: Whether to reuse the variables

Returns:

dict: The output dict

data_stat(data)[source]

get_ntypes()[source]

get_rcut()[source]

get_type_map()[source]

init_variables(graph: Graph, graph_def: GraphDef, model_type: str = 'original_model', suffix: str = '') → None[source]

Init the embedding net variables with the given frozen model.

Parameters:

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
model_typestr: the type of the model
suffixstr: suffix to name scope

model_type = 'multi_task'

class deepmd.model.PolarModel(*args, **kwargs)[source]

Bases: TensorModel

Methods

`build`(coord_, atype_, natoms, box, mesh, ...)	Build the model.
`build_descrpt`(coord_, atype_, natoms, box, ...)	Build the descriptor part of the model.
`init_variables`(graph, graph_def[, ...])	Init the embedding net variables with the given frozen model.

data_stat
get_ntypes
get_out_size
get_rcut
get_sel_type
get_type_map

class deepmd.model.WFCModel(*args, **kwargs)[source]

Bases: TensorModel

Methods

`build`(coord_, atype_, natoms, box, mesh, ...)	Build the model.
`build_descrpt`(coord_, atype_, natoms, box, ...)	Build the descriptor part of the model.
`init_variables`(graph, graph_def[, ...])	Init the embedding net variables with the given frozen model.

data_stat
get_ntypes
get_out_size
get_rcut
get_sel_type
get_type_map

Submodules

deepmd.model.ener module

class deepmd.model.ener.EnerModel(descrpt, fitting, typeebd=None, type_map: List[str] | None = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01, use_srtab: str | None = None, smin_alpha: float | None = None, sw_rmin: float | None = None, sw_rmax: float | None = None)[source]

Bases: Model

Energy model.

Parameters:

descrpt: Descriptor
fitting: Fitting net
type_map: Mapping atom type to the name (str) of the type. For example type_map[1] gives the name of the type 1.
data_stat_nbatch: Number of frames used for data statistic
data_stat_protect: Protect parameter for atomic energy regression
use_srtab: The table for the short-range pairwise interaction added on top of DP. The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. The first colume is the distance between atoms. The second to the last columes are energies for pairs of certain types. For example we have two atom types, 0 and 1. The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly.
smin_alpha: The short-range tabulated interaction will be swithed according to the distance of the nearest neighbor. This distance is calculated by softmin. This parameter is the decaying parameter in the softmin. It is only required when use_srtab is provided.
sw_rmin: The lower boundary of the interpolation between short-range tabulated interaction and DP. It is only required when use_srtab is provided.
sw_rmin: The upper boundary of the interpolation between short-range tabulated interaction and DP. It is only required when use_srtab is provided.

Methods

`build`(coord_, atype_, natoms, box, mesh, ...)	Build the model.
`build_descrpt`(coord_, atype_, natoms, box, ...)	Build the descriptor part of the model.
`init_variables`(graph, graph_def[, ...])	Init the embedding net variables with the given frozen model.

data_stat
get_ntypes
get_rcut
get_type_map

build(coord_, atype_, natoms, box, mesh, input_dict, frz_model=None, ckpt_meta: str | None = None, suffix='', reuse=None)[source]

Build the model.

Parameters:

coord_tf.Tensor: The coordinates of atoms
atype_tf.Tensor: The atom types of atoms
natomstf.Tensor: The number of atoms
boxtf.Tensor: The box vectors
meshtf.Tensor: The mesh vectors
input_dictdict: The input dict
frz_modelstr, optional: The path to the frozen model
ckpt_metastr, optional: The path to the checkpoint and meta file
suffixstr, optional: The suffix of the scope
reusebool or tf.AUTO_REUSE, optional: Whether to reuse the variables

Returns:

dict: The output dict

data_stat(data)[source]

get_ntypes()[source]

get_rcut()[source]

get_type_map()[source]

init_variables(graph: Graph, graph_def: GraphDef, model_type: str = 'original_model', suffix: str = '') → None[source]

Init the embedding net variables with the given frozen model.

Parameters:

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
model_typestr: the type of the model
suffixstr: suffix to name scope

model_type = 'ener'

deepmd.model.model module

class deepmd.model.model.Model[source]

Bases: ABC

Methods

`build`(coord_, atype_, natoms, box, mesh, ...)	Build the model.
`build_descrpt`(coord_, atype_, natoms, box, ...)	Build the descriptor part of the model.
`init_variables`(graph, graph_def[, ...])	Init the embedding net variables with the given frozen model.

abstract build(coord_: Tensor, atype_: Tensor, natoms: Tensor, box: Tensor, mesh: Tensor, input_dict: dict, frz_model: str | None = None, ckpt_meta: str | None = None, suffix: str = '', reuse: bool | Enum | None = None)[source]

Build the model.

Parameters:

coord_tf.Tensor: The coordinates of atoms
atype_tf.Tensor: The atom types of atoms
natomstf.Tensor: The number of atoms
boxtf.Tensor: The box vectors
meshtf.Tensor: The mesh vectors
input_dictdict: The input dict
frz_modelstr, optional: The path to the frozen model
ckpt_metastr, optional: The path to the checkpoint and meta file
suffixstr, optional: The suffix of the scope
reusebool or tf.AUTO_REUSE, optional: Whether to reuse the variables

Returns:

dict: The output dict

build_descrpt(coord_: Tensor, atype_: Tensor, natoms: Tensor, box: Tensor, mesh: Tensor, input_dict: dict, frz_model: str | None = None, ckpt_meta: str | None = None, suffix: str = '', reuse: bool | Enum | None = None)[source]

Build the descriptor part of the model.

Parameters:

coord_tf.Tensor: The coordinates of atoms
atype_tf.Tensor: The atom types of atoms
natomstf.Tensor: The number of atoms
boxtf.Tensor: The box vectors
meshtf.Tensor: The mesh vectors
input_dictdict: The input dict
frz_modelstr, optional: The path to the frozen model
ckpt_metastr, optional: The path to the checkpoint and meta file
suffixstr, optional: The suffix of the scope
reusebool or tf.AUTO_REUSE, optional: Whether to reuse the variables

Returns:

tf.Tensor: The descriptor tensor

init_variables(graph: Graph, graph_def: GraphDef, model_type: str = 'original_model', suffix: str = '') → None[source]

Init the embedding net variables with the given frozen model.

Parameters:

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
model_typestr: the type of the model
suffixstr: suffix to name scope

deepmd.model.model_stat module

deepmd.model.model_stat.make_stat_input(data, nbatches, merge_sys=True)[source]

Pack data for statistics.

Parameters:

data: The data
nbatchesint: The number of batches
merge_sysbool (True): Merge system data

Returns:

all_stat:

A dictionary of list of list storing data for stat. if merge_sys == False data can be accessed by

all_stat[key][sys_idx][batch_idx][frame_idx]

else merge_sys == True can be accessed by: all_stat[key][batch_idx][frame_idx]

deepmd.model.model_stat.merge_sys_stat(all_stat)[source]

deepmd.model.multi module

class deepmd.model.multi.MultiModel(descrpt, fitting_dict, fitting_type_dict, typeebd=None, type_map: List[str] | None = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01, use_srtab: str | None = None, smin_alpha: float | None = None, sw_rmin: float | None = None, sw_rmax: float | None = None)[source]

Bases: Model

Multi-task model.

Parameters:

descrpt: Descriptor
fitting_dict: Dictionary of fitting nets
fitting_type_dict: Dictionary of types of fitting nets
typeebd: Type embedding net
type_map: Mapping atom type to the name (str) of the type. For example type_map[1] gives the name of the type 1.
data_stat_nbatch: Number of frames used for data statistic
data_stat_protect: Protect parameter for atomic energy regression
use_srtab: The table for the short-range pairwise interaction added on top of DP. The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. The first colume is the distance between atoms. The second to the last columes are energies for pairs of certain types. For example we have two atom types, 0 and 1. The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly.
smin_alpha: The short-range tabulated interaction will be swithed according to the distance of the nearest neighbor. This distance is calculated by softmin. This parameter is the decaying parameter in the softmin. It is only required when use_srtab is provided.
sw_rmin: The lower boundary of the interpolation between short-range tabulated interaction and DP. It is only required when use_srtab is provided.
sw_rmin: The upper boundary of the interpolation between short-range tabulated interaction and DP. It is only required when use_srtab is provided.

Methods

`build`(coord_, atype_, natoms, box, mesh, ...)	Build the model.
`build_descrpt`(coord_, atype_, natoms, box, ...)	Build the descriptor part of the model.
`init_variables`(graph, graph_def[, ...])	Init the embedding net variables with the given frozen model.

data_stat
get_ntypes
get_rcut
get_type_map

build(coord_, atype_, natoms, box, mesh, input_dict, frz_model=None, ckpt_meta: str | None = None, suffix='', reuse=None)[source]

Build the model.

Parameters:

coord_tf.Tensor: The coordinates of atoms
atype_tf.Tensor: The atom types of atoms
natomstf.Tensor: The number of atoms
boxtf.Tensor: The box vectors
meshtf.Tensor: The mesh vectors
input_dictdict: The input dict
frz_modelstr, optional: The path to the frozen model
ckpt_metastr, optional: The path to the checkpoint and meta file
suffixstr, optional: The suffix of the scope
reusebool or tf.AUTO_REUSE, optional: Whether to reuse the variables

Returns:

dict: The output dict

data_stat(data)[source]

get_ntypes()[source]

get_rcut()[source]

get_type_map()[source]

init_variables(graph: Graph, graph_def: GraphDef, model_type: str = 'original_model', suffix: str = '') → None[source]

Init the embedding net variables with the given frozen model.

Parameters:

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
model_typestr: the type of the model
suffixstr: suffix to name scope

model_type = 'multi_task'

deepmd.model.tensor module

class deepmd.model.tensor.DipoleModel(*args, **kwargs)[source]

Bases: TensorModel

Methods

`build`(coord_, atype_, natoms, box, mesh, ...)	Build the model.
`build_descrpt`(coord_, atype_, natoms, box, ...)	Build the descriptor part of the model.
`init_variables`(graph, graph_def[, ...])	Init the embedding net variables with the given frozen model.

data_stat
get_ntypes
get_out_size
get_rcut
get_sel_type
get_type_map

class deepmd.model.tensor.GlobalPolarModel(*args, **kwargs)[source]

Bases: TensorModel

Methods

`build`(coord_, atype_, natoms, box, mesh, ...)	Build the model.
`build_descrpt`(coord_, atype_, natoms, box, ...)	Build the descriptor part of the model.
`init_variables`(graph, graph_def[, ...])	Init the embedding net variables with the given frozen model.

data_stat
get_ntypes
get_out_size
get_rcut
get_sel_type
get_type_map

class deepmd.model.tensor.PolarModel(*args, **kwargs)[source]

Bases: TensorModel

Methods

`build`(coord_, atype_, natoms, box, mesh, ...)	Build the model.
`build_descrpt`(coord_, atype_, natoms, box, ...)	Build the descriptor part of the model.
`init_variables`(graph, graph_def[, ...])	Init the embedding net variables with the given frozen model.

data_stat
get_ntypes
get_out_size
get_rcut
get_sel_type
get_type_map

class deepmd.model.tensor.TensorModel(tensor_name: str, descrpt, fitting, typeebd=None, type_map: List[str] | None = None, data_stat_nbatch: int = 10, data_stat_protect: float = 0.01)[source]

Bases: Model

Tensor model.

Parameters:

tensor_name: Name of the tensor.
descrpt: Descriptor
fitting: Fitting net
typeebd: Type embedding net
type_map: Mapping atom type to the name (str) of the type. For example type_map[1] gives the name of the type 1.
data_stat_nbatch: Number of frames used for data statistic
data_stat_protect: Protect parameter for atomic energy regression

Methods

`build`(coord_, atype_, natoms, box, mesh, ...)	Build the model.
`build_descrpt`(coord_, atype_, natoms, box, ...)	Build the descriptor part of the model.
`init_variables`(graph, graph_def[, ...])	Init the embedding net variables with the given frozen model.

data_stat
get_ntypes
get_out_size
get_rcut
get_sel_type
get_type_map

build(coord_, atype_, natoms, box, mesh, input_dict, frz_model=None, ckpt_meta: str | None = None, suffix='', reuse=None)[source]

Build the model.

Parameters:

coord_tf.Tensor: The coordinates of atoms
atype_tf.Tensor: The atom types of atoms
natomstf.Tensor: The number of atoms
boxtf.Tensor: The box vectors
meshtf.Tensor: The mesh vectors
input_dictdict: The input dict
frz_modelstr, optional: The path to the frozen model
ckpt_metastr, optional: The path to the checkpoint and meta file
suffixstr, optional: The suffix of the scope
reusebool or tf.AUTO_REUSE, optional: Whether to reuse the variables

Returns:

dict: The output dict

data_stat(data)[source]

get_ntypes()[source]

get_out_size()[source]

get_rcut()[source]

get_sel_type()[source]

get_type_map()[source]

init_variables(graph: Graph, graph_def: GraphDef, model_type: str = 'original_model', suffix: str = '') → None[source]

Init the embedding net variables with the given frozen model.

Parameters:

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
model_typestr: the type of the model
suffixstr: suffix to name scope

class deepmd.model.tensor.WFCModel(*args, **kwargs)[source]

Bases: TensorModel

Methods

`build`(coord_, atype_, natoms, box, mesh, ...)	Build the model.
`build_descrpt`(coord_, atype_, natoms, box, ...)	Build the descriptor part of the model.
`init_variables`(graph, graph_def[, ...])	Init the embedding net variables with the given frozen model.

data_stat
get_ntypes
get_out_size
get_rcut
get_sel_type
get_type_map