deepmd.tf.descriptor.se_a_ef

Classes

DescrptSeAEf	Smooth edition descriptor with Ef.
DescrptSeAEfLower	Helper class for implementing DescrptSeAEf.

Module Contents

class deepmd.tf.descriptor.se_a_ef.DescrptSeAEf(rcut: float, rcut_smth: float, sel: list[int], neuron: list[int] = [24, 48, 96], axis_neuron: int = 8, resnet_dt: bool = False, trainable: bool = True, seed: int | None = None, type_one_side: bool = True, exclude_types: list[list[int]] = [], set_davg_zero: bool = False, activation_function: str = 'tanh', precision: str = 'default', uniform_seed: bool = False, **kwargs: Any)

Bases: deepmd.tf.descriptor.se.DescrptSe

Smooth edition descriptor with Ef.

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

axis_neuron

Number of the axis neuron (number of columns of the sub-matrix of the embedding matrix)

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.

type_one_side

Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets

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.

set_davg_zero

Set the shift of embedding net input to zero.

activation_function

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

precision

The precision of the embedding net parameters. Supported options are “default”, “bfloat16”, “float64”, “float16”, “float32”.

uniform_seed

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

descrpt_para

descrpt_vert

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_dim_rot_mat_1() → int

Returns the first dimension of the rotation matrix. The rotation is of shape dim_1 x 3.

get_rot_mat() → deepmd.tf.env.tf.Tensor

Get rotational matrix.

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: Any) → None

Compute the statistics (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.

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. Should have ‘efield’.

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

class deepmd.tf.descriptor.se_a_ef.DescrptSeAEfLower(op: collections.abc.Callable, rcut: float, rcut_smth: float, sel: list[int], neuron: list[int] = [24, 48, 96], axis_neuron: int = 8, resnet_dt: bool = False, trainable: bool = True, seed: int | None = None, type_one_side: bool = True, exclude_types: list[list[int]] = [], set_davg_zero: bool = False, activation_function: str = 'tanh', precision: str = 'default', uniform_seed: bool = False)

Bases: deepmd.tf.descriptor.se_a.DescrptSeA

Helper class for implementing DescrptSeAEf.

sel_a

rcut_r

rcut_r_smth

filter_neuron = [24, 48, 96]

n_axis_neuron = 8

filter_resnet_dt = False

seed = None

trainable = True

op

sel_r

ntypes

rcut_a = -1

nnei_a

nnei_r

nnei

ndescrpt_a

ndescrpt_r

ndescrpt

useBN = False

dstd = None

davg = None

place_holders

sub_sess

compute_input_stats(data_coord: list[numpy.ndarray], data_box: list[numpy.ndarray], data_atype: list[numpy.ndarray], natoms_vec: list[numpy.ndarray], mesh: list[numpy.ndarray], input_dict: dict, **kwargs: Any) → None

Compute the statistics (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.

_normalize_3d(a: deepmd.tf.env.tf.Tensor) → deepmd.tf.env.tf.Tensor

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

_compute_dstats_sys_smth(data_coord: numpy.ndarray, data_box: numpy.ndarray, data_atype: numpy.ndarray, natoms_vec: numpy.ndarray, mesh: numpy.ndarray, data_efield: numpy.ndarray) → tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]

property input_requirement: list[deepmd.utils.data.DataRequirementItem]

Return data requirements needed for the model input.