deepmd.tf.descriptor.se_a_ef

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=False, **kwargs)[source]#

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 “none”, “gelu_tf”, “linear”, “relu6”, “sigmoid”, “tanh”, “gelu”, “relu”, “softplus”.
precision: The precision of the embedding net parameters. Supported options are “float16”, “float64”, “default”, “float32”.
uniform_seed: Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed

descrpt_para[source]#

descrpt_vert[source]#

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

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

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

get_dim_rot_mat_1() → int[source]#: 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[source]#: Get rotational matrix.

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

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[source]#

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.

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[source]#

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][source]#

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, 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)[source]#

Bases: deepmd.tf.descriptor.se_a.DescrptSeA

Helper class for implementing DescrptSeAEf.

sel_a[source]#

rcut_r[source]#

rcut_r_smth[source]#

filter_neuron[source]#

n_axis_neuron[source]#

filter_resnet_dt[source]#

seed[source]#

trainable[source]#

op[source]#

sel_r[source]#

ntypes[source]#

rcut_a[source]#

nnei_a[source]#

nnei_r[source]#

nnei[source]#

ndescrpt_a[source]#

ndescrpt_r[source]#

ndescrpt[source]#

useBN = False[source]#

dstd = None[source]#

davg = None[source]#

place_holders[source]#

sub_sess[source]#

compute_input_stats(data_coord, data_box, data_atype, natoms_vec, mesh, input_dict, **kwargs) → None[source]#

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.

_normalize_3d(a)[source]#

build(coord_, atype_, natoms, box_, mesh, input_dict, suffix='', reuse=None)[source]#

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, data_box, data_atype, natoms_vec, mesh, data_efield)[source]#

property input_requirement: list[deepmd.utils.data.DataRequirementItem][source]#: Return data requirements needed for the model input.