deepmd.dpmodel.descriptor.se_t_tebd

deepmd.dpmodel.descriptor.se_t_tebd#

Classes#

`DescrptSeTTebd`	Construct an embedding net that takes angles between two neighboring atoms and type embeddings as input.
`DescrptBlockSeTTebd`	The unit operation of a native model.

Module Contents#

class deepmd.dpmodel.descriptor.se_t_tebd.DescrptSeTTebd(rcut: float, rcut_smth: float, sel: list[int] | int, ntypes: int, neuron: list = [2, 4, 8], tebd_dim: int = 8, tebd_input_mode: str = 'concat', resnet_dt: bool = False, set_davg_zero: bool = True, 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, concat_output_tebd: bool = True, use_econf_tebd: bool = False, use_tebd_bias=False, smooth: bool = True)[source]#

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

Construct an embedding net that takes angles between two neighboring atoms and type embeddings as input.

Parameters:

rcut: The cut-off radius
rcut_smth: From where the environment matrix should be smoothed
selUnion[list[int], int]: list[int]: sel[i] specifies the maxmum number of type i atoms in the cut-off radius int: the total maxmum number of atoms in the cut-off radius
ntypesint: Number of element types
neuronlist[int]: Number of neurons in each hidden layers of the embedding net
tebd_dimint: Dimension of the type embedding
tebd_input_modestr: The input mode of the type embedding. Supported modes are [“concat”, “strip”]. - “concat”: Concatenate the type embedding with the smoothed angular information as the union input for the embedding network. - “strip”: Use a separated embedding network for the type embedding and combine the output with the angular embedding network output.
resnet_dt: Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
set_davg_zero: Set the shift of embedding net input to zero.
activation_function: The activation function in the embedding net. Supported options are “linear”, “sigmoid”, “relu6”, “softplus”, “none”, “gelu_tf”, “tanh”, “gelu”, “relu”.
env_protection: float: Protection parameter to prevent division by zero errors during environment matrix calculations.
exclude_typeslist[tuple[int, 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.
precision: The precision of the embedding net parameters. Supported options are “float32”, “float16”, “default”, “float64”.
trainable: If the weights of embedding net are trainable.
seed: Random seed for initializing the network parameters.
type_map: list[str], Optional: A list of strings. Give the name to each type of atoms.
concat_output_tebd: bool: Whether to concat type embedding at the output of the descriptor.
use_econf_tebd: bool, Optional: Whether to use electronic configuration type embedding.
use_tebd_biasbool, Optional: Whether to use bias in the type embedding layer.
smooth: bool: Whether to use smooth process in calculation.

se_ttebd[source]#

use_econf_tebd[source]#

type_map[source]#

smooth[source]#

type_embedding[source]#

tebd_dim[source]#

concat_output_tebd[source]#

trainable[source]#

precision[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 embedding dimension of g2.

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.

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.

abstract 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.

property dim_out[source]#

property dim_emb[source]#

abstract compute_input_stats(merged: list[dict], path: deepmd.utils.path.DPPath | None = None) → NoReturn[source]#: Update mean and stddev for descriptor elements.

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.

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.

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 x axis_neuron)
gr: The rotationally equivariant and permutationally invariant single particle representation. shape: nf x nloc x ng x 3
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) → DescrptSeTTebd[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

class deepmd.dpmodel.descriptor.se_t_tebd.DescrptBlockSeTTebd(rcut: float, rcut_smth: float, sel: list[int] | int, ntypes: int, neuron: list = [25, 50, 100], tebd_dim: int = 8, tebd_input_mode: str = 'concat', set_davg_zero: bool = True, activation_function='tanh', precision: str = 'float64', resnet_dt: bool = False, exclude_types: list[tuple[int, int]] = [], env_protection: float = 0.0, smooth: bool = True, seed: int | list[int] | None = None)[source]#

Bases: deepmd.dpmodel.NativeOP, deepmd.dpmodel.descriptor.descriptor.DescriptorBlock

The unit operation of a native model.

rcut[source]#

rcut_smth[source]#

neuron[source]#

filter_neuron[source]#

tebd_dim[source]#

tebd_input_mode[source]#

set_davg_zero[source]#

activation_function[source]#

precision[source]#

resnet_dt[source]#

env_protection[source]#

seed[source]#

smooth[source]#

ntypes[source]#

sel[source]#

sec[source]#

split_sel[source]#

nnei[source]#

ndescrpt[source]#

tebd_dim_input[source]#

embeddings[source]#

env_mat[source]#

mean[source]#

stddev[source]#

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

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

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

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

__getitem__(key)[source]#

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

property dim_emb[source]#: Returns the output dimension of embedding.

abstract compute_input_stats(merged: Callable[[], list[dict]] | list[dict], path: deepmd.utils.path.DPPath | None = None) → NoReturn[source]#: Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data.

abstract get_stats() → NoReturn[source]#: Get the statistics of the descriptor.

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

cal_g(ss, embedding_idx)[source]#

cal_g_strip(ss, embedding_idx)[source]#

call(nlist: numpy.ndarray, coord_ext: numpy.ndarray, atype_ext: numpy.ndarray, atype_embd_ext: numpy.ndarray | None = None, mapping: numpy.ndarray | None = None, type_embedding: numpy.ndarray | None = None)[source]#: Forward pass in NumPy implementation.

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.

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

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