deepmd.tf.descriptor.se_t

Classes

DescrptSeT

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

Module Contents

class deepmd.tf.descriptor.se_t.DescrptSeT(rcut: float, rcut_smth: float, sel: list[int], neuron: list[int] = [24, 48, 96], resnet_dt: bool = False, trainable: bool = True, seed: int | None = None, exclude_types: list[list[int]] = [], set_davg_zero: bool = False, activation_function: str = 'tanh', precision: str = 'default', uniform_seed: bool = False, type_map: list[str] | None = None, env_protection: float = 0.0, **kwargs)

Bases: deepmd.tf.descriptor.se.DescrptSe

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

The embedding takes angles between two neighboring atoms as input.

Parameters:

rcut

The cut-off radius

rcut_smth

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_dt

Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)

trainable

If the weights of embedding net are trainable.

seed

Random seed for initializing the network parameters.

set_davg_zero

Set the shift of embedding net input to zero.

activation_function

The activation function in the embedding net. Supported options are “relu”, “linear”, “tanh”, “sigmoid”, “gelu_tf”, “gelu”, “none”, “softplus”, “relu6”.

precision

The precision of the embedding net parameters. Supported options are “default”, “float32”, “float64”, “float16”.

uniform_seed

Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed

env_protection: float

Protection parameter to prevent division by zero errors during environment matrix calculations.

type_map: list[str], Optional

A list of strings. Give the name to each type of atoms.

sel_a

rcut_r

rcut_r_smth

filter_neuron

filter_resnet_dt

seed

uniform_seed

seed_shift

trainable

filter_activation_fn

activation_function_name

filter_precision

env_protection

orig_exclude_types

exclude_types

type_map

set_davg_zero

sel_r

ntypes

rcut_a

nnei_a

nnei_r

nnei

ndescrpt_a

ndescrpt_r

ndescrpt

useBN = False

dstd = None

davg = None

compress = False

embedding_net_variables = None

place_holders

sub_sess

get_rcut() → float

Returns the cut-off radius.

get_ntypes() → int

Returns the number of atom types.

get_dim_out() → int

Returns the output dimension of this descriptor.

get_nlist() → tuple[deepmd.tf.env.tf.Tensor, deepmd.tf.env.tf.Tensor, list[int], list[int]]

Returns neighbor information.

Returns:

nlist

Neighbor list

rij

The relative distance between the neighbor and the center atom.

sel_a

The number of neighbors with full information

sel_r

The number of neighbors with only radial information

compute_input_stats(data_coord: list, data_box: list, data_atype: list, natoms_vec: list, mesh: list, input_dict: dict, **kwargs) → None

Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.

Parameters:

data_coord

The coordinates. Can be generated by deepmd.tf.model.make_stat_input

data_box

The box. Can be generated by deepmd.tf.model.make_stat_input

data_atype

The atom types. Can be generated by deepmd.tf.model.make_stat_input

natoms_vec

The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.tf.model.make_stat_input

mesh

The mesh for neighbor searching. Can be generated by deepmd.tf.model.make_stat_input

input_dict

Dictionary for additional input

**kwargs

Additional keyword arguments.

merge_input_stats(stat_dict) → None

Merge the statisitcs computed from compute_input_stats to obtain the self.davg and self.dstd.

Parameters:

stat_dict

The dict of statisitcs computed from compute_input_stats, including:

sumr

The sum of radial statisitcs.

suma

The sum of relative coord statisitcs.

sumn

The sum of neighbor numbers.

sumr2

The sum of square of radial statisitcs.

suma2

The sum of square of relative coord statisitcs.

enable_compression(min_nbor_dist: float, graph: deepmd.tf.env.tf.Graph, graph_def: deepmd.tf.env.tf.GraphDef, table_extrapolate: float = 5, table_stride_1: float = 0.01, table_stride_2: float = 0.1, check_frequency: int = -1, suffix: str = '') → None

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

Parameters:

min_nbor_dist

The nearest distance between atoms

graphtf.Graph

The graph of the model

graph_deftf.GraphDef

The graph_def of the model

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

suffixstr, optional

The suffix of the scope

build(coord_: deepmd.tf.env.tf.Tensor, atype_: deepmd.tf.env.tf.Tensor, natoms: deepmd.tf.env.tf.Tensor, box_: deepmd.tf.env.tf.Tensor, mesh: deepmd.tf.env.tf.Tensor, input_dict: dict, reuse: bool | None = None, suffix: str = '') → deepmd.tf.env.tf.Tensor

Build the computational graph for the descriptor.

Parameters:

coord_

The coordinate of atoms

atype_

The type of atoms

natoms

The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

box_tf.Tensor

The box of the system

mesh

For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.

input_dict

Dictionary for additional inputs

reuse

The weights in the networks should be reused when get the variable.

suffix

Name suffix to identify this descriptor

Returns:

descriptor

The output descriptor

prod_force_virial(atom_ener: deepmd.tf.env.tf.Tensor, natoms: deepmd.tf.env.tf.Tensor) → tuple[deepmd.tf.env.tf.Tensor, deepmd.tf.env.tf.Tensor, deepmd.tf.env.tf.Tensor]

Compute force and virial.

Parameters:

atom_ener

The atomic energy

natoms

The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

Returns:

force

The force on atoms

virial

The total virial

atom_virial

The atomic virial

_pass_filter(inputs, atype, natoms, input_dict, reuse=None, suffix='', trainable=True)

_compute_dstats_sys_smth(data_coord, data_box, data_atype, natoms_vec, mesh)

_compute_std(sumv2, sumv, sumn)

_filter(inputs, type_input, natoms, activation_fn=tf.nn.tanh, stddev=1.0, bavg=0.0, name='linear', reuse=None, trainable=True)

serialize_network(ntypes: int, ndim: int, in_dim: int, neuron: list[int], activation_function: str, resnet_dt: bool, variables: dict, excluded_types: set[tuple[int, int]] = set(), suffix: str = '') → dict

Serialize network.

Parameters:

ntypesint

The number of types

ndimint

The dimension of elements

in_dimint

The input dimension

neuronlist[int]

The neuron list

activation_functionstr

The activation function

resnet_dtbool

Whether to use resnet

variablesdict

The input variables

excluded_typesset[tuple[int, int]], optional

The excluded types

suffixstr, optional

The suffix of the scope

Returns:

dict

The converted network data

classmethod deserialize_network(data: dict, suffix: str = '') → dict

Deserialize network.

Parameters:

datadict

The input network data

suffixstr, optional

The suffix of the scope

Returns:

variablesdict

The input variables

classmethod deserialize(data: dict, suffix: str = '')

Deserialize the model.

Parameters:

datadict

The serialized data

Returns:

Model

The deserialized model

serialize(suffix: str = '') → dict

Serialize the model.

Parameters:

suffixstr, optional

The suffix of the scope

Returns:

dict

The serialized data