deepmd.dpmodel.descriptor.se_t

deepmd.dpmodel.descriptor.se_t#

Classes#

DescrptSeT

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

Module Contents#

class deepmd.dpmodel.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 = DEFAULT_PRECISION, trainable: bool = True, seed: int | list[int] | None = None, type_map: list[str] | None = None, ntypes: int | None = None)[source]#

Bases: deepmd.dpmodel.NativeOP, deepmd.dpmodel.descriptor.base_descriptor.BaseDescriptor

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 “silut”, “silu”, “relu”, “relu6”, “sigmoid”, “linear”, “tanh”, “none”, “softplus”, “gelu”, “gelu_tf”.
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 “float32”, “default”, “float16”, “float64”.
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.
ntypesint: Number of element types. Not used in this descriptor, only to be compat with input.

rcut[source]#

rcut_smth[source]#

sel[source]#

neuron = [24, 48, 96][source]#

filter_neuron = [24, 48, 96][source]#

set_davg_zero = False[source]#

activation_function = 'tanh'[source]#

precision = 'float64'[source]#

prec[source]#

resnet_dt = False[source]#

env_protection = 0.0[source]#

ntypes[source]#

seed = None[source]#

type_map = None[source]#

trainable = True[source]#

sel_cumsum[source]#

embeddings[source]#

env_mat[source]#

nnei[source]#

davg[source]#

dstd[source]#

orig_sel[source]#

ndescrpt[source]#

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

__getitem__(key)[source]#

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

abstractmethod 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.

get_dim_out()[source]#: Returns the output dimension of this descriptor.

get_dim_emb()[source]#: Returns the embedding (g2) dimension of this descriptor.

get_rcut()[source]#: Returns cutoff radius.

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

get_sel()[source]#: Returns cutoff radius.

mixed_types() → bool[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.

abstractmethod share_params(base_class, shared_level, resume=False) → NoReturn[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.

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

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

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: paddle.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.

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

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

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

call(coord_ext, atype_ext, nlist, mapping: numpy.ndarray | 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 from extended to local region. not used by this descriptor.

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.

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

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

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