deepmd.tf.descriptor.se_r

deepmd.tf.descriptor.se_r#

Classes#

DescrptSeR

DeepPot-SE constructed from radial information of atomic configurations.

Module Contents#

class deepmd.tf.descriptor.se_r.DescrptSeR(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, 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, spin: deepmd.tf.utils.spin.Spin | None = None, type_map: list[str] | None = None, env_protection: float = 0.0, **kwargs)[source]#

Bases: deepmd.tf.descriptor.se.DescrptSe

DeepPot-SE constructed from radial information of atomic configurations.

The embedding takes the distance between 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.
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.
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
type_map: list[str], Optional: A list of strings. Give the name to each type of atoms.

sel_r[source]#

rcut[source]#

rcut_smth[source]#

filter_neuron[source]#

filter_resnet_dt[source]#

seed[source]#

uniform_seed[source]#

seed_shift[source]#

trainable[source]#

filter_activation_fn[source]#

activation_function_name[source]#

filter_precision[source]#

orig_exclude_types[source]#

exclude_types[source]#

env_protection[source]#

type_map[source]#

set_davg_zero[source]#

type_one_side[source]#

spin[source]#

sel_a[source]#

ntypes[source]#

nnei_a[source]#

nnei_r[source]#

nnei[source]#

ndescrpt_a[source]#

ndescrpt_r[source]#

ndescrpt[source]#

useBN = False[source]#

davg = None[source]#

dstd = None[source]#

compress = False[source]#

embedding_net_variables = None[source]#

place_holders[source]#

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

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

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

get_nlist()[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, 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.

merge_input_stats(stat_dict) → None[source]#

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.
sumn: The sum of neighbor numbers.
sumr2: The sum of square of radial 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[source]#

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[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

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

_pass_filter(inputs, atype, natoms, reuse=None, suffix='', trainable=True)[source]#

_compute_dstats_sys_se_r(data_coord, data_box, data_atype, natoms_vec, mesh)[source]#

_compute_std(sumv2, sumv, sumn)[source]#

_filter_r(inputs, type_input, natoms, activation_fn=tf.nn.tanh, stddev=1.0, bavg=0.0, name='linear', reuse=None, trainable=True)[source]#

classmethod deserialize(data: dict, suffix: str = '')[source]#

Deserialize the model.

Parameters:

datadict: The serialized data

Returns:

Model: The deserialized model

serialize(suffix: str = '') → dict[source]#

Serialize the model.

Parameters:

suffixstr, optional: The suffix of the scope

Returns:

dict: The serialized data