deepmd.pt.model.descriptor.se_t

deepmd.pt.model.descriptor.se_t#

Classes#

`DescrptSeT`	DeepPot-SE constructed from all information (both angular and radial) of atomic
`DescrptBlockSeT`	The building block of descriptor.

Functions#

tabulate_fusion_se_t(→ list[torch.Tensor])

Module Contents#

deepmd.pt.model.descriptor.se_t.tabulate_fusion_se_t(argument0: torch.Tensor, argument1: torch.Tensor, argument2: torch.Tensor, argument3: torch.Tensor, argument4: int) → list[torch.Tensor][source]#

class deepmd.pt.model.descriptor.se_t.DescrptSeT(rcut: float, rcut_smth: float, sel: list[int], neuron: list[int] = [24, 48, 96], resnet_dt: bool = False, set_davg_zero: bool = False, activation_function: str = 'tanh', env_protection: float = 0.0, exclude_types: list[tuple[int, int]] = [], precision: str = 'float64', trainable: bool = True, seed: int | list[int] | None = None, type_map: list[str] | None = None, ntypes: int | None = None, spin=None)[source]#

Bases: deepmd.pt.model.descriptor.base_descriptor.BaseDescriptor, torch.nn.Module

DeepPot-SE constructed from all information (both angular and radial) of atomic configurations.

The embedding takes angles between two neighboring atoms as input.

Parameters:

rcutfloat: The cut-off radius
rcut_smthfloat: From where the environment matrix should be smoothed
sellist[int]: sel[i] specifies the maxmum number of type i atoms in the cut-off radius
neuronlist[int]: Number of neurons in each hidden layers of the embedding net
resnet_dtbool: Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
set_davg_zerobool: Set the shift of embedding net input to zero.
activation_functionstr: The activation function in the embedding net. Supported options are “relu”, “linear”, “tanh”, “sigmoid”, “gelu_tf”, “gelu”, “none”, “softplus”, “relu6”.
env_protectionfloat: Protection parameter to prevent division by zero errors during environment matrix calculations.
exclude_typeslist[list[int]]: The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.
precisionstr: The precision of the embedding net parameters. Supported options are “default”, “float32”, “float64”, “float16”.
trainablebool: If the weights of embedding net are trainable.
seedint, Optional: Random seed for initializing the network parameters.
type_map: list[str], Optional: A list of strings. Give the name to each type of atoms.

type_map[source]#

compress = False[source]#

prec[source]#

seat[source]#

get_rcut() → float[source]#: Returns the cut-off radius.

get_rcut_smth() → float[source]#: Returns the radius where the neighbor information starts to smoothly decay to 0.

get_nsel() → int[source]#: Returns the number of selected atoms in the cut-off radius.

get_sel() → list[int][source]#: Returns the number of selected atoms for each type.

get_ntypes() → int[source]#: Returns the number of element types.

get_type_map() → list[str][source]#: Get the name to each type of atoms.

get_dim_out() → int[source]#: Returns the output dimension.

get_dim_emb() → int[source]#: Returns the output dimension.

mixed_types()[source]#: Returns if the descriptor requires a neighbor list that distinguish different atomic types or not.

has_message_passing() → bool[source]#: Returns whether the descriptor has message passing.

need_sorted_nlist_for_lower() → bool[source]#: Returns whether the descriptor needs sorted nlist when using forward_lower.

get_env_protection() → float[source]#: Returns the protection of building environment matrix.

share_params(base_class, shared_level, resume=False) → None[source]#: Share the parameters of self to the base_class with shared_level during multitask training. If not start from checkpoint (resume is False), some separated parameters (e.g. mean and stddev) will be re-calculated across different classes.

property dim_out[source]#: Returns the output dimension of this descriptor.

abstract change_type_map(type_map: list[str], model_with_new_type_stat=None) → None[source]#: Change the type related params to new ones, according to type_map and the original one in the model. If there are new types in type_map, statistics will be updated accordingly to model_with_new_type_stat for these new types.

compute_input_stats(merged: Callable[[], list[dict]] | list[dict], path: deepmd.utils.path.DPPath | None = None)[source]#

Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data.

Parameters:

mergedUnion[Callable[[], list[dict]], list[dict]]

list[dict]: A list of data samples from various data systems.
Each element, merged[i], is a data dictionary containing keys: torch.Tensor originating from the i-th data system.
Callable[[], list[dict]]: A lazy function that returns data samples in the above format
only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once.

pathOptional[DPPath]

The path to the stat file.

enable_compression(min_nbor_dist: float, table_extrapolate: float = 5, table_stride_1: float = 0.01, table_stride_2: float = 0.1, check_frequency: int = -1) → None[source]#

Receive the statisitcs (distance, max_nbor_size and env_mat_range) of the training data.

Parameters:

min_nbor_dist: The nearest distance between atoms
table_extrapolate: The scale of model extrapolation
table_stride_1: The uniform stride of the first table
table_stride_2: The uniform stride of the second table
check_frequency: The overflow check frequency

reinit_exclude(exclude_types: list[tuple[int, int]] = []) → None[source]#: Update the type exclusions.

forward(coord_ext: torch.Tensor, atype_ext: torch.Tensor, nlist: torch.Tensor, mapping: torch.Tensor | None = None, comm_dict: dict[str, torch.Tensor] | None = None)[source]#

Compute the descriptor.

Parameters:

coord_ext: The extended coordinates of atoms. shape: nf x (nallx3)
atype_ext: The extended aotm types. shape: nf x nall
nlist: The neighbor list. shape: nf x nloc x nnei
mapping: The index mapping, not required by this descriptor.
comm_dict: The data needed for communication for parallel inference.

Returns:

descriptor: The descriptor. shape: nf x nloc x ng
gr: The rotationally equivariant and permutationally invariant single particle representation. This descriptor returns None.
g2: The rotationally invariant pair-partical representation. This descriptor returns None.
h2: The rotationally equivariant pair-partical representation. This descriptor returns None.
sw: The smooth switch function.

set_stat_mean_and_stddev(mean: torch.Tensor, stddev: torch.Tensor) → None[source]#: Update mean and stddev for descriptor.

get_stat_mean_and_stddev() → tuple[torch.Tensor, torch.Tensor][source]#: Get mean and stddev for descriptor.

serialize() → dict[source]#: Serialize the obj to dict.

classmethod deserialize(data: dict) → DescrptSeT[source]#

Deserialize the model.

Parameters:

datadict: The serialized data

Returns:

BD: The deserialized descriptor

classmethod update_sel(train_data: deepmd.utils.data_system.DeepmdDataSystem, type_map: list[str] | None, local_jdata: dict) → tuple[dict, float | None][source]#

Update the selection and perform neighbor statistics.

Parameters:

train_dataDeepmdDataSystem: data used to do neighbor statistics
type_maplist[str], optional: The name of each type of atoms
local_jdatadict: The local data refer to the current class

Returns:

dict: The updated local data
float: The minimum distance between two atoms

class deepmd.pt.model.descriptor.se_t.DescrptBlockSeT(rcut: float, rcut_smth: float, sel: list[int], neuron: list[int] = [24, 48, 96], resnet_dt: bool = False, set_davg_zero: bool = False, activation_function: str = 'tanh', env_protection: float = 0.0, exclude_types: list[tuple[int, int]] = [], precision: str = 'float64', trainable: bool = True, seed: int | list[int] | None = None)[source]#

Bases: deepmd.pt.model.descriptor.DescriptorBlock

The building block of descriptor. Given the input descriptor, provide with the atomic coordinates, atomic types and neighbor list, calculate the new descriptor.

ndescrpt: Final[int][source]#

__constants__: ClassVar[list] = ['ndescrpt'][source]#

rcut[source]#

rcut_smth[source]#

neuron[source]#

filter_neuron[source]#

set_davg_zero[source]#

activation_function[source]#

precision[source]#

prec[source]#

resnet_dt[source]#

env_protection[source]#

ntypes[source]#

seed[source]#

sel[source]#

sec[source]#

split_sel[source]#

nnei[source]#

filter_layers[source]#

stats = None[source]#

trainable[source]#

compress = False[source]#

compress_info[source]#

compress_data[source]#

get_rcut() → float[source]#: Returns the cut-off radius.

get_rcut_smth() → float[source]#: Returns the radius where the neighbor information starts to smoothly decay to 0.

get_nsel() → int[source]#: Returns the number of selected atoms in the cut-off radius.

get_sel() → list[int][source]#: Returns the number of selected atoms for each type.

get_ntypes() → int[source]#: Returns the number of element types.

get_dim_out() → int[source]#: Returns the output dimension.

get_dim_emb() → int[source]#: Returns the output dimension.

get_dim_in() → int[source]#: Returns the input dimension.

mixed_types() → bool[source]#

If true, the descriptor 1. assumes total number of atoms aligned across frames; 2. requires a neighbor list that does not distinguish different atomic types.

If false, the descriptor 1. assumes total number of atoms of each atom type aligned across frames; 2. requires a neighbor list that distinguishes different atomic types.

get_env_protection() → float[source]#: Returns the protection of building environment matrix.

property dim_out[source]#: Returns the output dimension of this descriptor.

property dim_in: int[source]#: Returns the atomic input dimension of this descriptor.

__setitem__(key, value) → None[source]#

__getitem__(key)[source]#

compute_input_stats(merged: Callable[[], list[dict]] | list[dict], path: deepmd.utils.path.DPPath | None = None) → None[source]#

Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data.

Parameters:

mergedUnion[Callable[[], list[dict]], list[dict]]

list[dict]: A list of data samples from various data systems.
Each element, merged[i], is a data dictionary containing keys: torch.Tensor originating from the i-th data system.
Callable[[], list[dict]]: A lazy function that returns data samples in the above format
only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once.

pathOptional[DPPath]

The path to the stat file.

get_stats() → dict[str, deepmd.utils.env_mat_stat.StatItem][source]#: Get the statistics of the descriptor.

reinit_exclude(exclude_types: list[tuple[int, int]] = []) → None[source]#

enable_compression(table_data, table_config, lower, upper) → None[source]#

forward(nlist: torch.Tensor, extended_coord: torch.Tensor, extended_atype: torch.Tensor, extended_atype_embd: torch.Tensor | None = None, mapping: torch.Tensor | None = None)[source]#

Compute the descriptor.

Parameters:

nlist: The neighbor list. shape: nf x nloc x nnei
extended_coord: The extended coordinates of atoms. shape: nf x (nallx3)
extended_atype: The extended aotm types. shape: nf x nall x nt
extended_atype_embd: The extended type embedding of atoms. shape: nf x nall
mapping: The index mapping, not required by this descriptor.

Returns:

result: The descriptor. shape: nf x nloc x ng
gr: The rotationally equivariant and permutationally invariant single particle representation. This descriptor returns None.
g2: The rotationally invariant pair-partical representation. This descriptor returns None.
h2: The rotationally equivariant pair-partical representation. This descriptor returns None.
sw: The smooth switch function. shape: nf x nloc x nnei

has_message_passing() → bool[source]#: Returns whether the descriptor block has message passing.

need_sorted_nlist_for_lower() → bool[source]#: Returns whether the descriptor block needs sorted nlist when using forward_lower.