deepmd.tf.descriptor.loc_frame

Classes

DescrptLocFrame

Defines a local frame at each atom, and the compute the descriptor as local

Module Contents

class deepmd.tf.descriptor.loc_frame.DescrptLocFrame(rcut: float, sel_a: list[int], sel_r: list[int], axis_rule: list[int], **kwargs)

Bases: deepmd.tf.descriptor.descriptor.Descriptor

Defines a local frame at each atom, and the compute the descriptor as local coordinates under this frame.

Parameters:

rcut

The cut-off radius

sel_alist[int]

The length of the list should be the same as the number of atom types in the system. sel_a[i] gives the selected number of type-i neighbors. The full relative coordinates of the neighbors are used by the descriptor.

sel_rlist[int]

The length of the list should be the same as the number of atom types in the system. sel_r[i] gives the selected number of type-i neighbors. Only relative distance of the neighbors are used by the descriptor. sel_a[i] + sel_r[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius.

axis_rule: list[int]

The length should be 6 times of the number of types. - axis_rule[i*6+0]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance. - axis_rule[i*6+1]: type of the atom defining the first axis of type-i atom. - axis_rule[i*6+2]: index of the axis atom defining the first axis. Note that the neighbors with the same class and type are sorted according to their relative distance. - axis_rule[i*6+3]: class of the atom defining the second axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance. - axis_rule[i*6+4]: type of the atom defining the second axis of type-i atom. - axis_rule[i*6+5]: index of the axis atom defining the second axis. Note that the neighbors with the same class and type are sorted according to their relative distance.

sel_a

sel_r

axis_rule

rcut_r

ntypes

rcut_a

nnei_a

nnei_r

nnei

ndescrpt_a

ndescrpt_r

ndescrpt

davg = None

dstd = 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:

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.

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

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

Get rotational matrix.

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

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

_compute_std(sumv2, sumv, sumn)

init_variables(graph: deepmd.tf.env.tf.Graph, graph_def: deepmd.tf.env.tf.GraphDef, suffix: str = '') → None

Init the embedding net variables with the given dict.

Parameters:

graphtf.Graph

The input frozen model graph

graph_deftf.GraphDef

The input frozen model graph_def

suffixstr, optional

The suffix of the scope

classmethod update_sel(train_data: deepmd.utils.data_system.DeepmdDataSystem, type_map: list[str] | None, local_jdata: dict) → tuple[dict, float | None]

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