deepmd.infer package

Submodule containing all the implemented potentials.

class deepmd.infer.DeepDOS(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: Union[bool, int, AutoBatchSize] = True, input_map: Optional[dict] = None)

Bases: DeepEval

Constructor.

Parameters

model_filePath

The name of the frozen model file.

load_prefix: str

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

auto_batch_sizebool or int or AutomaticBatchSize, default: True

If True, automatic batch size will be used. If int, it will be used as the initial batch size.

input_mapdict, optional

The input map for tf.import_graph_def. Only work with default tf graph

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval(coords, cells, atom_types[, atomic, ...])	Evaluate the dos, atom_dos by using this model.
eval_descriptor(coords, cells, atom_types[, ...])	Evaluate descriptors by using this DP.
eval_typeebd()	Evaluate output of type embedding network by using this model.
get_dim_aparam()	Get the number (dimension) of atomic parameters of this DP.
get_dim_fparam()	Get the number (dimension) of frame parameters of this DP.
get_ntypes()	Get the number of atom types of this model.
get_numb_dos()	Get the length of DOS output of this DP model.
get_rcut()	Get the cut-off radius of this model.
get_sel_type()	Unsupported in this model.
get_type_map()	Get the type map (element name of the atom types) of this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

eval(coords: ndarray, cells: ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, mixed_type: bool = False) → Tuple[ndarray, ...]

Evaluate the dos, atom_dos by using this model.

Parameters

coords

The coordinates of atoms. The array should be of size nframes x natoms x 3

cells

The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9

atom_types

The atom types The list should contain natoms ints

atomic

Calculate the atomic energy and virial

fparam

The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.

aparam

The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.

mixed_type

Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns

dos

The electron density of state.

atom_dos

The atom-sited density of state. Only returned when atomic == True

eval_descriptor(coords: ndarray, cells: ndarray, atom_types: List[int], fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, efield: Optional[ndarray] = None, mixed_type: bool = False) → array

Evaluate descriptors by using this DP.

Parameters

coords

The coordinates of atoms. The array should be of size nframes x natoms x 3

cells

The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9

atom_types

The atom types The list should contain natoms ints

fparam

The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.

aparam

The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.

efield

The external field on atoms. The array should be of size nframes x natoms x 3

mixed_type

Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns

descriptor

Descriptors.

get_dim_aparam() → int

Get the number (dimension) of atomic parameters of this DP.

get_dim_fparam() → int

Get the number (dimension) of frame parameters of this DP.

get_ntypes() → int

Get the number of atom types of this model.

get_numb_dos() → int

Get the length of DOS output of this DP model.

get_rcut() → float

Get the cut-off radius of this model.

get_sel_type() → List[int]

Unsupported in this model.

get_type_map() → List[str]

Get the type map (element name of the atom types) of this model.

class deepmd.infer.DeepDipole(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, input_map: Optional[dict] = None, neighbor_list=None)

Bases: DeepTensor

Constructor.

Parameters

model_filePath

The name of the frozen model file.

load_prefix: str

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

input_mapdict, optional

The input map for tf.import_graph_def. Only work with default tf graph

neighbor_listase.neighborlist.NeighborList, optional

The neighbor list object. If None, then build the native neighbor list.

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
eval_full(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
eval_typeebd()	Evaluate output of type embedding network by using this model.
get_dim_aparam()	Unsupported in this model.
get_dim_fparam()	Unsupported in this model.
get_ntypes()	Get the number of atom types of this model.
get_rcut()	Get the cut-off radius of this model.
get_sel_type()	Get the selected atom types of this model.
get_type_map()	Get the type map (element name of the atom types) of this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

get_dim_aparam() → int

Unsupported in this model.

get_dim_fparam() → int

Unsupported in this model.

class deepmd.infer.DeepEval(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: Union[bool, int, AutoBatchSize] = False, input_map: Optional[dict] = None, neighbor_list=None)

Bases: object

Common methods for DeepPot, DeepWFC, DeepPolar, …

Parameters

model_filePath

The name of the frozen model file.

load_prefix: str

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

auto_batch_sizebool or int or AutomaticBatchSize, default: False

If True, automatic batch size will be used. If int, it will be used as the initial batch size.

input_mapdict, optional

The input map for tf.import_graph_def. Only work with default tf graph

neighbor_listase.neighborlist.NewPrimitiveNeighborList, optional

The ASE neighbor list class to produce the neighbor list. If None, the neighbor list will be built natively in the model.

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval_typeebd()	Evaluate output of type embedding network by using this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

build_neighbor_list(coords: ndarray, cell: Optional[ndarray], atype: ndarray, imap: ndarray, neighbor_list)

Make the mesh with neighbor list for a single frame.

Parameters

coordsnp.ndarray

The coordinates of atoms. Should be of shape [natoms, 3]

cellOptional[np.ndarray]

The cell of the system. Should be of shape [3, 3]

atypenp.ndarray

The type of atoms. Should be of shape [natoms]

imapnp.ndarray

The index map of atoms. Should be of shape [natoms]

neighbor_listase.neighborlist.NewPrimitiveNeighborList

ASE neighbor list. The following method or attribute will be used/set: bothways, self_interaction, update, build, first_neigh, pair_second, offset_vec.

Returns

natoms_vecnp.ndarray

The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: nloc natoms[1]: nall natoms[i]: 2 <= i < Ntypes+2, number of type i atoms for nloc

coordsnp.ndarray

The coordinates of atoms, including ghost atoms. Should be of shape [nframes, nall, 3]

atypenp.ndarray

The type of atoms, including ghost atoms. Should be of shape [nall]

meshnp.ndarray

The mesh in nei_mode=4.

imapnp.ndarray

The index map of atoms. Should be of shape [nall]

ghost_mapnp.ndarray

The index map of ghost atoms. Should be of shape [nghost]

eval_typeebd() → ndarray

Evaluate output of type embedding network by using this model.

Returns

np.ndarray

The output of type embedding network. The shape is [ntypes, o_size], where ntypes is the number of types, and o_size is the number of nodes in the output layer.

Raises

KeyError

If the model does not enable type embedding.

See also

deepmd.utils.type_embed.TypeEmbedNet

The type embedding network.

Examples

Get the output of type embedding network of graph.pb:

>>> from deepmd.infer import DeepPotential
>>> dp = DeepPotential('graph.pb')
>>> dp.eval_typeebd()

load_prefix: str

make_natoms_vec(atom_types: ndarray, mixed_type: bool = False) → ndarray

Make the natom vector used by deepmd-kit.

Parameters

atom_types

The type of atoms

mixed_type

Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns

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

property model_type: str

Get type of model.

:type:str

property model_version: str

Get version of model.

Returns

str

version of model

static reverse_map(vec: ndarray, imap: List[int]) → ndarray

Reverse mapping of a vector according to the index map.

Parameters

vec

Input vector. Be of shape [nframes, natoms, -1]

imap

Index map. Be of shape [natoms]

Returns

vec_out

Reverse mapped vector.

property sess: Session

Get TF session.

static sort_input(coord: ndarray, atom_type: ndarray, sel_atoms: Optional[List[int]] = None, mixed_type: bool = False)

Sort atoms in the system according their types.

Parameters

coord

The coordinates of atoms. Should be of shape [nframes, natoms, 3]

atom_type

The type of atoms Should be of shape [natoms]

sel_atoms

The selected atoms by type

mixed_type

Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns

coord_out

The coordinates after sorting

atom_type_out

The atom types after sorting

idx_map

The index mapping from the input to the output. For example coord_out = coord[:,idx_map,:]

sel_atom_type

Only output if sel_atoms is not None The sorted selected atom types

sel_idx_map

Only output if sel_atoms is not None The index mapping from the selected atoms to sorted selected atoms.

class deepmd.infer.DeepGlobalPolar(model_file: str, load_prefix: str = 'load', default_tf_graph: bool = False, neighbor_list=None)

Bases: DeepTensor

Constructor.

Parameters

model_filestr

The name of the frozen model file.

load_prefix: str

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

neighbor_listase.neighborlist.NeighborList, optional

The neighbor list object. If None, then build the native neighbor list.

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
eval_full(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
eval_typeebd()	Evaluate output of type embedding network by using this model.
get_dim_aparam()	Unsupported in this model.
get_dim_fparam()	Unsupported in this model.
get_ntypes()	Get the number of atom types of this model.
get_rcut()	Get the cut-off radius of this model.
get_sel_type()	Get the selected atom types of this model.
get_type_map()	Get the type map (element name of the atom types) of this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

eval(coords: ndarray, cells: ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, efield: Optional[ndarray] = None) → ndarray

Evaluate the model.

Parameters

coords

The coordinates of atoms. The array should be of size nframes x natoms x 3

cells

The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9

atom_types

The atom types The list should contain natoms ints

atomic

Not used in this model

fparam

Not used in this model

aparam

Not used in this model

efield

Not used in this model

Returns

tensor

The returned tensor If atomic == False then of size nframes x variable_dof else of size nframes x natoms x variable_dof

get_dim_aparam() → int

Unsupported in this model.

get_dim_fparam() → int

Unsupported in this model.

class deepmd.infer.DeepPolar(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, input_map: Optional[dict] = None, neighbor_list=None)

Bases: DeepTensor

Constructor.

Parameters

model_filePath

The name of the frozen model file.

load_prefix: str

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

input_mapdict, optional

The input map for tf.import_graph_def. Only work with default tf graph

neighbor_listase.neighborlist.NeighborList, optional

The neighbor list object. If None, then build the native neighbor list.

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
eval_full(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
eval_typeebd()	Evaluate output of type embedding network by using this model.
get_dim_aparam()	Unsupported in this model.
get_dim_fparam()	Unsupported in this model.
get_ntypes()	Get the number of atom types of this model.
get_rcut()	Get the cut-off radius of this model.
get_sel_type()	Get the selected atom types of this model.
get_type_map()	Get the type map (element name of the atom types) of this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

get_dim_aparam() → int

Unsupported in this model.

get_dim_fparam() → int

Unsupported in this model.

class deepmd.infer.DeepPot(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: Union[bool, int, AutoBatchSize] = True, input_map: Optional[dict] = None, neighbor_list=None)

Bases: DeepEval

Constructor.

Parameters

model_filePath

The name of the frozen model file.

load_prefix: str

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

auto_batch_sizebool or int or AutomaticBatchSize, default: True

If True, automatic batch size will be used. If int, it will be used as the initial batch size.

input_mapdict, optional

The input map for tf.import_graph_def. Only work with default tf graph

neighbor_listase.neighborlist.NewPrimitiveNeighborList, optional

The ASE neighbor list class to produce the neighbor list. If None, the neighbor list will be built natively in the model.

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Examples

>>> from deepmd.infer import DeepPot
>>> import numpy as np
>>> dp = DeepPot('graph.pb')
>>> coord = np.array([[1,0,0], [0,0,1.5], [1,0,3]]).reshape([1, -1])
>>> cell = np.diag(10 * np.ones(3)).reshape([1, -1])
>>> atype = [1,0,1]
>>> e, f, v = dp.eval(coord, cell, atype)

where e, f and v are predicted energy, force and virial of the system, respectively.

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval(coords, cells, atom_types[, atomic, ...])	Evaluate the energy, force and virial by using this DP.
eval_descriptor(coords, cells, atom_types[, ...])	Evaluate descriptors by using this DP.
eval_typeebd()	Evaluate output of type embedding network by using this model.
get_descriptor_type()	Get the descriptor type of this model.
get_dim_aparam()	Get the number (dimension) of atomic parameters of this DP.
get_dim_fparam()	Get the number (dimension) of frame parameters of this DP.
get_ntypes()	Get the number of atom types of this model.
get_ntypes_spin()	Get the number of spin atom types of this model.
get_rcut()	Get the cut-off radius of this model.
get_sel_type()	Unsupported in this model.
get_type_map()	Get the type map (element name of the atom types) of this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

eval(coords: ndarray, cells: ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, efield: Optional[ndarray] = None, mixed_type: bool = False) → Tuple[ndarray, ...]

Evaluate the energy, force and virial by using this DP.

Parameters

coords

The coordinates of atoms. The array should be of size nframes x natoms x 3

cells

The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9

atom_types

The atom types The list should contain natoms ints

atomic

Calculate the atomic energy and virial

fparam

The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.

aparam

The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.

efield

The external field on atoms. The array should be of size nframes x natoms x 3

mixed_type

Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns

energy

The system energy.

force

The force on each atom

virial

The virial

atom_energy

The atomic energy. Only returned when atomic == True

atom_virial

The atomic virial. Only returned when atomic == True

eval_descriptor(coords: ndarray, cells: ndarray, atom_types: List[int], fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, efield: Optional[ndarray] = None, mixed_type: bool = False) → array

Evaluate descriptors by using this DP.

Parameters

coords

The coordinates of atoms. The array should be of size nframes x natoms x 3

cells

The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9

atom_types

The atom types The list should contain natoms ints

fparam

The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.

aparam

The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.

efield

The external field on atoms. The array should be of size nframes x natoms x 3

mixed_type

Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns

descriptor

Descriptors.

get_descriptor_type() → List[int]

Get the descriptor type of this model.

get_dim_aparam() → int

Get the number (dimension) of atomic parameters of this DP.

get_dim_fparam() → int

Get the number (dimension) of frame parameters of this DP.

get_ntypes() → int

Get the number of atom types of this model.

get_ntypes_spin()

Get the number of spin atom types of this model.

get_rcut() → float

Get the cut-off radius of this model.

get_sel_type() → List[int]

Unsupported in this model.

get_type_map() → List[str]

Get the type map (element name of the atom types) of this model.

deepmd.infer.DeepPotential(model_file: Union[str, Path], load_prefix: str = 'load', default_tf_graph: bool = False, input_map: Optional[dict] = None, neighbor_list=None) → Union[DeepDipole, DeepGlobalPolar, DeepPolar, DeepPot, DeepDOS, DeepWFC]

Factory function that will inialize appropriate potential read from model_file.

Parameters

model_filestr

The name of the frozen model file.

load_prefixstr

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

input_mapdict, optional

The input map for tf.import_graph_def. Only work with default tf graph

neighbor_listase.neighborlist.NeighborList, optional

The neighbor list object. If None, then build the native neighbor list.

Returns

Union[DeepDipole, DeepGlobalPolar, DeepPolar, DeepPot, DeepWFC]

one of the available potentials

Raises

RuntimeError

if model file does not correspond to any implementd potential

class deepmd.infer.DeepWFC(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, input_map: Optional[dict] = None)

Bases: DeepTensor

Constructor.

Parameters

model_filePath

The name of the frozen model file.

load_prefix: str

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

input_mapdict, optional

The input map for tf.import_graph_def. Only work with default tf graph

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
eval_full(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
eval_typeebd()	Evaluate output of type embedding network by using this model.
get_dim_aparam()	Unsupported in this model.
get_dim_fparam()	Unsupported in this model.
get_ntypes()	Get the number of atom types of this model.
get_rcut()	Get the cut-off radius of this model.
get_sel_type()	Get the selected atom types of this model.
get_type_map()	Get the type map (element name of the atom types) of this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

get_dim_aparam() → int

Unsupported in this model.

get_dim_fparam() → int

Unsupported in this model.

class deepmd.infer.DipoleChargeModifier(model_name: str, model_charge_map: List[float], sys_charge_map: List[float], ewald_h: float = 1, ewald_beta: float = 1)

Bases: DeepDipole

Parameters

model_name

The model file for the DeepDipole model

model_charge_map

Gives the amount of charge for the wfcc

sys_charge_map

Gives the amount of charge for the real atoms

ewald_h

Grid spacing of the reciprocal part of Ewald sum. Unit: A

ewald_beta

Splitting parameter of the Ewald sum. Unit: A^{-1}

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_fv_graph()	Build the computational graph for the force and virial inference.
build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval(coord, box, atype[, eval_fv])	Evaluate the modification.
eval_full(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
eval_typeebd()	Evaluate output of type embedding network by using this model.
get_dim_aparam()	Unsupported in this model.
get_dim_fparam()	Unsupported in this model.
get_ntypes()	Get the number of atom types of this model.
get_rcut()	Get the cut-off radius of this model.
get_sel_type()	Get the selected atom types of this model.
get_type_map()	Get the type map (element name of the atom types) of this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
modify_data(data, data_sys)	Modify data.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

build_fv_graph() → Tensor

Build the computational graph for the force and virial inference.

eval(coord: ndarray, box: ndarray, atype: ndarray, eval_fv: bool = True) → Tuple[ndarray, ndarray, ndarray]

Evaluate the modification.

Parameters

coord

The coordinates of atoms

box

The simulation region. PBC is assumed

atype

The atom types

eval_fv

Evaluate force and virial

Returns

tot_e

The energy modification

tot_f

The force modification

tot_v

The virial modification

modify_data(data: dict, data_sys: DeepmdData) → None

Modify data.

Parameters

data

Internal data of DeepmdData. Be a dict, has the following keys - coord coordinates - box simulation box - type atom types - find_energy tells if data has energy - find_force tells if data has force - find_virial tells if data has virial - energy energy - force force - virial virial

data_sysDeepmdData

The data system.

class deepmd.infer.EwaldRecp(hh, beta)

Bases: object

Evaluate the reciprocal part of the Ewald sum.

Methods

eval(coord, charge, box)

Evaluate.

eval(coord: ndarray, charge: ndarray, box: ndarray) → Tuple[ndarray, ndarray, ndarray]

Evaluate.

Parameters

coord

The coordinates of atoms

charge

The atomic charge

box

The simulation region. PBC is assumed

Returns

e

The energy

f

The force

v

The virial

deepmd.infer.calc_model_devi(coord, box, atype, models, fname=None, frequency=1, mixed_type=False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, real_data: Optional[dict] = None, atomic: bool = False, relative: Optional[float] = None, relative_v: Optional[float] = None)

Python interface to calculate model deviation.

Parameters

coordnumpy.ndarray, n_frames x n_atoms x 3

Coordinates of system to calculate

boxnumpy.ndarray or None, n_frames x 3 x 3

Box to specify periodic boundary condition. If None, no pbc will be used

atypenumpy.ndarray, n_atoms x 1

Atom types

modelslist of DeepPot models

Models used to evaluate deviation

fnamestr or None

File to dump results, default None

frequencyint

Steps between frames (if the system is given by molecular dynamics engine), default 1

mixed_typebool

Whether the input atype is in mixed_type format or not

fparamnumpy.ndarray

frame specific parameters

aparamnumpy.ndarray

atomic specific parameters

real_datadict, optional

real data to calculate RMS real error

atomicbool, default: False

If True, calculate the force model deviation of each atom.

relativefloat, default: None

If given, calculate the relative model deviation of force. The value is the level parameter for computing the relative model deviation of the force.

relative_vfloat, default: None

If given, calculate the relative model deviation of virial. The value is the level parameter for computing the relative model deviation of the virial.

Returns

model_devinumpy.ndarray, n_frames x 8

Model deviation results. The first column is index of steps, the other 7 columns are max_devi_v, min_devi_v, avg_devi_v, max_devi_f, min_devi_f, avg_devi_f, devi_e.

Examples

>>> from deepmd.infer import calc_model_devi
>>> from deepmd.infer import DeepPot as DP
>>> import numpy as np
>>> coord = np.array([[1,0,0], [0,0,1.5], [1,0,3]]).reshape([1, -1])
>>> cell = np.diag(10 * np.ones(3)).reshape([1, -1])
>>> atype = [1,0,1]
>>> graphs = [DP("graph.000.pb"), DP("graph.001.pb")]
>>> model_devi = calc_model_devi(coord, cell, atype, graphs)

Submodules

deepmd.infer.data_modifier module

class deepmd.infer.data_modifier.DipoleChargeModifier(model_name: str, model_charge_map: List[float], sys_charge_map: List[float], ewald_h: float = 1, ewald_beta: float = 1)

Bases: DeepDipole

Parameters

model_name

The model file for the DeepDipole model

model_charge_map

Gives the amount of charge for the wfcc

sys_charge_map

Gives the amount of charge for the real atoms

ewald_h

Grid spacing of the reciprocal part of Ewald sum. Unit: A

ewald_beta

Splitting parameter of the Ewald sum. Unit: A^{-1}

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_fv_graph()	Build the computational graph for the force and virial inference.
build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval(coord, box, atype[, eval_fv])	Evaluate the modification.
eval_full(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
eval_typeebd()	Evaluate output of type embedding network by using this model.
get_dim_aparam()	Unsupported in this model.
get_dim_fparam()	Unsupported in this model.
get_ntypes()	Get the number of atom types of this model.
get_rcut()	Get the cut-off radius of this model.
get_sel_type()	Get the selected atom types of this model.
get_type_map()	Get the type map (element name of the atom types) of this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
modify_data(data, data_sys)	Modify data.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

build_fv_graph() → Tensor

Build the computational graph for the force and virial inference.

eval(coord: ndarray, box: ndarray, atype: ndarray, eval_fv: bool = True) → Tuple[ndarray, ndarray, ndarray]

Evaluate the modification.

Parameters

coord

The coordinates of atoms

box

The simulation region. PBC is assumed

atype

The atom types

eval_fv

Evaluate force and virial

Returns

tot_e

The energy modification

tot_f

The force modification

tot_v

The virial modification

modify_data(data: dict, data_sys: DeepmdData) → None

Modify data.

Parameters

data

Internal data of DeepmdData. Be a dict, has the following keys - coord coordinates - box simulation box - type atom types - find_energy tells if data has energy - find_force tells if data has force - find_virial tells if data has virial - energy energy - force force - virial virial

data_sysDeepmdData

The data system.

deepmd.infer.deep_dipole module

class deepmd.infer.deep_dipole.DeepDipole(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, input_map: Optional[dict] = None, neighbor_list=None)

Bases: DeepTensor

Constructor.

Parameters

model_filePath

The name of the frozen model file.

load_prefix: str

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

input_mapdict, optional

The input map for tf.import_graph_def. Only work with default tf graph

neighbor_listase.neighborlist.NeighborList, optional

The neighbor list object. If None, then build the native neighbor list.

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
eval_full(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
eval_typeebd()	Evaluate output of type embedding network by using this model.
get_dim_aparam()	Unsupported in this model.
get_dim_fparam()	Unsupported in this model.
get_ntypes()	Get the number of atom types of this model.
get_rcut()	Get the cut-off radius of this model.
get_sel_type()	Get the selected atom types of this model.
get_type_map()	Get the type map (element name of the atom types) of this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

get_dim_aparam() → int

Unsupported in this model.

get_dim_fparam() → int

Unsupported in this model.

deepmd.infer.deep_dos module

class deepmd.infer.deep_dos.DeepDOS(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: Union[bool, int, AutoBatchSize] = True, input_map: Optional[dict] = None)

Bases: DeepEval

Constructor.

Parameters

model_filePath

The name of the frozen model file.

load_prefix: str

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

auto_batch_sizebool or int or AutomaticBatchSize, default: True

If True, automatic batch size will be used. If int, it will be used as the initial batch size.

input_mapdict, optional

The input map for tf.import_graph_def. Only work with default tf graph

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval(coords, cells, atom_types[, atomic, ...])	Evaluate the dos, atom_dos by using this model.
eval_descriptor(coords, cells, atom_types[, ...])	Evaluate descriptors by using this DP.
eval_typeebd()	Evaluate output of type embedding network by using this model.
get_dim_aparam()	Get the number (dimension) of atomic parameters of this DP.
get_dim_fparam()	Get the number (dimension) of frame parameters of this DP.
get_ntypes()	Get the number of atom types of this model.
get_numb_dos()	Get the length of DOS output of this DP model.
get_rcut()	Get the cut-off radius of this model.
get_sel_type()	Unsupported in this model.
get_type_map()	Get the type map (element name of the atom types) of this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

eval(coords: ndarray, cells: ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, mixed_type: bool = False) → Tuple[ndarray, ...]

Evaluate the dos, atom_dos by using this model.

Parameters

coords

The coordinates of atoms. The array should be of size nframes x natoms x 3

cells

The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9

atom_types

The atom types The list should contain natoms ints

atomic

Calculate the atomic energy and virial

fparam

The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.

aparam

The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.

mixed_type

Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns

dos

The electron density of state.

atom_dos

The atom-sited density of state. Only returned when atomic == True

eval_descriptor(coords: ndarray, cells: ndarray, atom_types: List[int], fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, efield: Optional[ndarray] = None, mixed_type: bool = False) → array

Evaluate descriptors by using this DP.

Parameters

coords

The coordinates of atoms. The array should be of size nframes x natoms x 3

cells

The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9

atom_types

The atom types The list should contain natoms ints

fparam

The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.

aparam

The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.

efield

The external field on atoms. The array should be of size nframes x natoms x 3

mixed_type

Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns

descriptor

Descriptors.

get_dim_aparam() → int

Get the number (dimension) of atomic parameters of this DP.

get_dim_fparam() → int

Get the number (dimension) of frame parameters of this DP.

get_ntypes() → int

Get the number of atom types of this model.

get_numb_dos() → int

Get the length of DOS output of this DP model.

get_rcut() → float

Get the cut-off radius of this model.

get_sel_type() → List[int]

Unsupported in this model.

get_type_map() → List[str]

Get the type map (element name of the atom types) of this model.

deepmd.infer.deep_eval module

class deepmd.infer.deep_eval.DeepEval(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: Union[bool, int, AutoBatchSize] = False, input_map: Optional[dict] = None, neighbor_list=None)

Bases: object

Common methods for DeepPot, DeepWFC, DeepPolar, …

Parameters

model_filePath

The name of the frozen model file.

load_prefix: str

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

auto_batch_sizebool or int or AutomaticBatchSize, default: False

If True, automatic batch size will be used. If int, it will be used as the initial batch size.

input_mapdict, optional

The input map for tf.import_graph_def. Only work with default tf graph

neighbor_listase.neighborlist.NewPrimitiveNeighborList, optional

The ASE neighbor list class to produce the neighbor list. If None, the neighbor list will be built natively in the model.

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval_typeebd()	Evaluate output of type embedding network by using this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

build_neighbor_list(coords: ndarray, cell: Optional[ndarray], atype: ndarray, imap: ndarray, neighbor_list)

Make the mesh with neighbor list for a single frame.

Parameters

coordsnp.ndarray

The coordinates of atoms. Should be of shape [natoms, 3]

cellOptional[np.ndarray]

The cell of the system. Should be of shape [3, 3]

atypenp.ndarray

The type of atoms. Should be of shape [natoms]

imapnp.ndarray

The index map of atoms. Should be of shape [natoms]

neighbor_listase.neighborlist.NewPrimitiveNeighborList

ASE neighbor list. The following method or attribute will be used/set: bothways, self_interaction, update, build, first_neigh, pair_second, offset_vec.

Returns

natoms_vecnp.ndarray

The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: nloc natoms[1]: nall natoms[i]: 2 <= i < Ntypes+2, number of type i atoms for nloc

coordsnp.ndarray

The coordinates of atoms, including ghost atoms. Should be of shape [nframes, nall, 3]

atypenp.ndarray

The type of atoms, including ghost atoms. Should be of shape [nall]

meshnp.ndarray

The mesh in nei_mode=4.

imapnp.ndarray

The index map of atoms. Should be of shape [nall]

ghost_mapnp.ndarray

The index map of ghost atoms. Should be of shape [nghost]

eval_typeebd() → ndarray

Evaluate output of type embedding network by using this model.

Returns

np.ndarray

The output of type embedding network. The shape is [ntypes, o_size], where ntypes is the number of types, and o_size is the number of nodes in the output layer.

Raises

KeyError

If the model does not enable type embedding.

See also

deepmd.utils.type_embed.TypeEmbedNet

The type embedding network.

Examples

Get the output of type embedding network of graph.pb:

>>> from deepmd.infer import DeepPotential
>>> dp = DeepPotential('graph.pb')
>>> dp.eval_typeebd()

load_prefix: str

make_natoms_vec(atom_types: ndarray, mixed_type: bool = False) → ndarray

Make the natom vector used by deepmd-kit.

Parameters

atom_types

The type of atoms

mixed_type

Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns

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

property model_type: str

Get type of model.

:type:str

property model_version: str

Get version of model.

Returns

str

version of model

static reverse_map(vec: ndarray, imap: List[int]) → ndarray

Reverse mapping of a vector according to the index map.

Parameters

vec

Input vector. Be of shape [nframes, natoms, -1]

imap

Index map. Be of shape [natoms]

Returns

vec_out

Reverse mapped vector.

property sess: Session

Get TF session.

static sort_input(coord: ndarray, atom_type: ndarray, sel_atoms: Optional[List[int]] = None, mixed_type: bool = False)

Sort atoms in the system according their types.

Parameters

coord

The coordinates of atoms. Should be of shape [nframes, natoms, 3]

atom_type

The type of atoms Should be of shape [natoms]

sel_atoms

The selected atoms by type

mixed_type

Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns

coord_out

The coordinates after sorting

atom_type_out

The atom types after sorting

idx_map

The index mapping from the input to the output. For example coord_out = coord[:,idx_map,:]

sel_atom_type

Only output if sel_atoms is not None The sorted selected atom types

sel_idx_map

Only output if sel_atoms is not None The index mapping from the selected atoms to sorted selected atoms.

deepmd.infer.deep_polar module

class deepmd.infer.deep_polar.DeepGlobalPolar(model_file: str, load_prefix: str = 'load', default_tf_graph: bool = False, neighbor_list=None)

Bases: DeepTensor

Constructor.

Parameters

model_filestr

The name of the frozen model file.

load_prefix: str

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

neighbor_listase.neighborlist.NeighborList, optional

The neighbor list object. If None, then build the native neighbor list.

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
eval_full(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
eval_typeebd()	Evaluate output of type embedding network by using this model.
get_dim_aparam()	Unsupported in this model.
get_dim_fparam()	Unsupported in this model.
get_ntypes()	Get the number of atom types of this model.
get_rcut()	Get the cut-off radius of this model.
get_sel_type()	Get the selected atom types of this model.
get_type_map()	Get the type map (element name of the atom types) of this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

eval(coords: ndarray, cells: ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, efield: Optional[ndarray] = None) → ndarray

Evaluate the model.

Parameters

coords

The coordinates of atoms. The array should be of size nframes x natoms x 3

cells

The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9

atom_types

The atom types The list should contain natoms ints

atomic

Not used in this model

fparam

Not used in this model

aparam

Not used in this model

efield

Not used in this model

Returns

tensor

The returned tensor If atomic == False then of size nframes x variable_dof else of size nframes x natoms x variable_dof

get_dim_aparam() → int

Unsupported in this model.

get_dim_fparam() → int

Unsupported in this model.

class deepmd.infer.deep_polar.DeepPolar(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, input_map: Optional[dict] = None, neighbor_list=None)

Bases: DeepTensor

Constructor.

Parameters

model_filePath

The name of the frozen model file.

load_prefix: str

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

input_mapdict, optional

The input map for tf.import_graph_def. Only work with default tf graph

neighbor_listase.neighborlist.NeighborList, optional

The neighbor list object. If None, then build the native neighbor list.

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
eval_full(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
eval_typeebd()	Evaluate output of type embedding network by using this model.
get_dim_aparam()	Unsupported in this model.
get_dim_fparam()	Unsupported in this model.
get_ntypes()	Get the number of atom types of this model.
get_rcut()	Get the cut-off radius of this model.
get_sel_type()	Get the selected atom types of this model.
get_type_map()	Get the type map (element name of the atom types) of this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

get_dim_aparam() → int

Unsupported in this model.

get_dim_fparam() → int

Unsupported in this model.

deepmd.infer.deep_pot module

class deepmd.infer.deep_pot.DeepPot(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: Union[bool, int, AutoBatchSize] = True, input_map: Optional[dict] = None, neighbor_list=None)

Bases: DeepEval

Constructor.

Parameters

model_filePath

The name of the frozen model file.

load_prefix: str

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

auto_batch_sizebool or int or AutomaticBatchSize, default: True

If True, automatic batch size will be used. If int, it will be used as the initial batch size.

input_mapdict, optional

The input map for tf.import_graph_def. Only work with default tf graph

neighbor_listase.neighborlist.NewPrimitiveNeighborList, optional

The ASE neighbor list class to produce the neighbor list. If None, the neighbor list will be built natively in the model.

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Examples

>>> from deepmd.infer import DeepPot
>>> import numpy as np
>>> dp = DeepPot('graph.pb')
>>> coord = np.array([[1,0,0], [0,0,1.5], [1,0,3]]).reshape([1, -1])
>>> cell = np.diag(10 * np.ones(3)).reshape([1, -1])
>>> atype = [1,0,1]
>>> e, f, v = dp.eval(coord, cell, atype)

where e, f and v are predicted energy, force and virial of the system, respectively.

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval(coords, cells, atom_types[, atomic, ...])	Evaluate the energy, force and virial by using this DP.
eval_descriptor(coords, cells, atom_types[, ...])	Evaluate descriptors by using this DP.
eval_typeebd()	Evaluate output of type embedding network by using this model.
get_descriptor_type()	Get the descriptor type of this model.
get_dim_aparam()	Get the number (dimension) of atomic parameters of this DP.
get_dim_fparam()	Get the number (dimension) of frame parameters of this DP.
get_ntypes()	Get the number of atom types of this model.
get_ntypes_spin()	Get the number of spin atom types of this model.
get_rcut()	Get the cut-off radius of this model.
get_sel_type()	Unsupported in this model.
get_type_map()	Get the type map (element name of the atom types) of this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

eval(coords: ndarray, cells: ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, efield: Optional[ndarray] = None, mixed_type: bool = False) → Tuple[ndarray, ...]

Evaluate the energy, force and virial by using this DP.

Parameters

coords

The coordinates of atoms. The array should be of size nframes x natoms x 3

cells

The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9

atom_types

The atom types The list should contain natoms ints

atomic

Calculate the atomic energy and virial

fparam

The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.

aparam

The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.

efield

The external field on atoms. The array should be of size nframes x natoms x 3

mixed_type

Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns

energy

The system energy.

force

The force on each atom

virial

The virial

atom_energy

The atomic energy. Only returned when atomic == True

atom_virial

The atomic virial. Only returned when atomic == True

eval_descriptor(coords: ndarray, cells: ndarray, atom_types: List[int], fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, efield: Optional[ndarray] = None, mixed_type: bool = False) → array

Evaluate descriptors by using this DP.

Parameters

coords

The coordinates of atoms. The array should be of size nframes x natoms x 3

cells

The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9

atom_types

The atom types The list should contain natoms ints

fparam

The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.

aparam

The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.

efield

The external field on atoms. The array should be of size nframes x natoms x 3

mixed_type

Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns

descriptor

Descriptors.

get_descriptor_type() → List[int]

Get the descriptor type of this model.

get_dim_aparam() → int

Get the number (dimension) of atomic parameters of this DP.

get_dim_fparam() → int

Get the number (dimension) of frame parameters of this DP.

get_ntypes() → int

Get the number of atom types of this model.

get_ntypes_spin()

Get the number of spin atom types of this model.

get_rcut() → float

Get the cut-off radius of this model.

get_sel_type() → List[int]

Unsupported in this model.

get_type_map() → List[str]

Get the type map (element name of the atom types) of this model.

deepmd.infer.deep_tensor module

class deepmd.infer.deep_tensor.DeepTensor(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, input_map: Optional[dict] = None, neighbor_list=None)

Bases: DeepEval

Evaluates a tensor model.

Parameters

model_file: str

The name of the frozen model file.

load_prefix: str

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

input_mapdict, optional

The input map for tf.import_graph_def. Only work with default tf graph

neighbor_listase.neighborlist.NeighborList, optional

The neighbor list object. If None, then build the native neighbor list.

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
eval_full(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
eval_typeebd()	Evaluate output of type embedding network by using this model.
get_dim_aparam()	Get the number (dimension) of atomic parameters of this DP.
get_dim_fparam()	Get the number (dimension) of frame parameters of this DP.
get_ntypes()	Get the number of atom types of this model.
get_rcut()	Get the cut-off radius of this model.
get_sel_type()	Get the selected atom types of this model.
get_type_map()	Get the type map (element name of the atom types) of this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

eval(coords: ndarray, cells: ndarray, atom_types: List[int], atomic: bool = True, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, efield: Optional[ndarray] = None, mixed_type: bool = False) → ndarray

Evaluate the model.

Parameters

coords

The coordinates of atoms. The array should be of size nframes x natoms x 3

cells

The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9

atom_types

The atom types The list should contain natoms ints

atomic

If True (default), return the atomic tensor Otherwise return the global tensor

fparam

Not used in this model

aparam

Not used in this model

efield

Not used in this model

mixed_type

Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns

tensor

The returned tensor If atomic == False then of size nframes x output_dim else of size nframes x natoms x output_dim

eval_full(coords: ndarray, cells: ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[array] = None, aparam: Optional[array] = None, efield: Optional[array] = None, mixed_type: bool = False) → Tuple[ndarray, ...]

Evaluate the model with interface similar to the energy model. Will return global tensor, component-wise force and virial and optionally atomic tensor and atomic virial.

Parameters

coords

The coordinates of atoms. The array should be of size nframes x natoms x 3

cells

The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9

atom_types

The atom types The list should contain natoms ints

atomic

Whether to calculate atomic tensor and virial

fparam

Not used in this model

aparam

Not used in this model

efield

Not used in this model

mixed_type

Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns

tensor

The global tensor. shape: [nframes x nout]

force

The component-wise force (negative derivative) on each atom. shape: [nframes x nout x natoms x 3]

virial

The component-wise virial of the tensor. shape: [nframes x nout x 9]

atom_tensor

The atomic tensor. Only returned when atomic == True shape: [nframes x natoms x nout]

atom_virial

The atomic virial. Only returned when atomic == True shape: [nframes x nout x natoms x 9]

get_dim_aparam() → int

Get the number (dimension) of atomic parameters of this DP.

get_dim_fparam() → int

Get the number (dimension) of frame parameters of this DP.

get_ntypes() → int

Get the number of atom types of this model.

get_rcut() → float

Get the cut-off radius of this model.

get_sel_type() → List[int]

Get the selected atom types of this model.

get_type_map() → List[str]

Get the type map (element name of the atom types) of this model.

tensors: ClassVar[Dict[str, str]] = {'t_box': 't_box:0', 't_coord': 't_coord:0', 't_mesh': 't_mesh:0', 't_natoms': 't_natoms:0', 't_ntypes': 'descrpt_attr/ntypes:0', 't_ouput_dim': 'model_attr/output_dim:0', 't_rcut': 'descrpt_attr/rcut:0', 't_sel_type': 'model_attr/sel_type:0', 't_tmap': 'model_attr/tmap:0', 't_type': 't_type:0'}

deepmd.infer.deep_wfc module

class deepmd.infer.deep_wfc.DeepWFC(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, input_map: Optional[dict] = None)

Bases: DeepTensor

Constructor.

Parameters

model_filePath

The name of the frozen model file.

load_prefix: str

The prefix in the load computational graph

default_tf_graphbool

If uses the default tf graph, otherwise build a new tf graph for evaluation

input_mapdict, optional

The input map for tf.import_graph_def. Only work with default tf graph

Warning

For developers: DeepTensor initializer must be called at the end after self.tensors are modified because it uses the data in self.tensors dict. Do not chanage the order!

Attributes

model_type

Get type of model.

model_version

Get version of model.

sess

Get TF session.

Methods

build_neighbor_list(coords, cell, atype, ...)	Make the mesh with neighbor list for a single frame.
eval(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
eval_full(coords, cells, atom_types[, ...])	Evaluate the model with interface similar to the energy model.
eval_typeebd()	Evaluate output of type embedding network by using this model.
get_dim_aparam()	Unsupported in this model.
get_dim_fparam()	Unsupported in this model.
get_ntypes()	Get the number of atom types of this model.
get_rcut()	Get the cut-off radius of this model.
get_sel_type()	Get the selected atom types of this model.
get_type_map()	Get the type map (element name of the atom types) of this model.
make_natoms_vec(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
reverse_map(vec, imap)	Reverse mapping of a vector according to the index map.
sort_input(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

get_dim_aparam() → int

Unsupported in this model.

get_dim_fparam() → int

Unsupported in this model.

deepmd.infer.ewald_recp module

class deepmd.infer.ewald_recp.EwaldRecp(hh, beta)

Bases: object

Evaluate the reciprocal part of the Ewald sum.

Methods

eval(coord, charge, box)

Evaluate.

eval(coord: ndarray, charge: ndarray, box: ndarray) → Tuple[ndarray, ndarray, ndarray]

Evaluate.

Parameters

coord

The coordinates of atoms

charge

The atomic charge

box

The simulation region. PBC is assumed

Returns

e

The energy

f

The force

v

The virial

deepmd.infer.model_devi module

deepmd.infer.model_devi.calc_model_devi(coord, box, atype, models, fname=None, frequency=1, mixed_type=False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, real_data: Optional[dict] = None, atomic: bool = False, relative: Optional[float] = None, relative_v: Optional[float] = None)

Python interface to calculate model deviation.

Parameters

coordnumpy.ndarray, n_frames x n_atoms x 3

Coordinates of system to calculate

boxnumpy.ndarray or None, n_frames x 3 x 3

Box to specify periodic boundary condition. If None, no pbc will be used

atypenumpy.ndarray, n_atoms x 1

Atom types

modelslist of DeepPot models

Models used to evaluate deviation

fnamestr or None

File to dump results, default None

frequencyint

Steps between frames (if the system is given by molecular dynamics engine), default 1

mixed_typebool

Whether the input atype is in mixed_type format or not

fparamnumpy.ndarray

frame specific parameters

aparamnumpy.ndarray

atomic specific parameters

real_datadict, optional

real data to calculate RMS real error

atomicbool, default: False

If True, calculate the force model deviation of each atom.

relativefloat, default: None

If given, calculate the relative model deviation of force. The value is the level parameter for computing the relative model deviation of the force.

relative_vfloat, default: None

If given, calculate the relative model deviation of virial. The value is the level parameter for computing the relative model deviation of the virial.

Returns

model_devinumpy.ndarray, n_frames x 8

Model deviation results. The first column is index of steps, the other 7 columns are max_devi_v, min_devi_v, avg_devi_v, max_devi_f, min_devi_f, avg_devi_f, devi_e.

Examples

>>> from deepmd.infer import calc_model_devi
>>> from deepmd.infer import DeepPot as DP
>>> import numpy as np
>>> coord = np.array([[1,0,0], [0,0,1.5], [1,0,3]]).reshape([1, -1])
>>> cell = np.diag(10 * np.ones(3)).reshape([1, -1])
>>> atype = [1,0,1]
>>> graphs = [DP("graph.000.pb"), DP("graph.001.pb")]
>>> model_devi = calc_model_devi(coord, cell, atype, graphs)

deepmd.infer.model_devi.calc_model_devi_e(es: ndarray, real_e: Optional[ndarray] = None) → ndarray

Calculate model deviation of total energy per atom.

Here we don’t use the atomic energy, as the decomposition of energy is arbitrary and not unique. There is no fitting target for atomic energy.

Parameters

esnumpy.ndarray

size of `n_models x n_frames x 1

real_enumpy.ndarray

real energy, size of n_frames x 1. If given, the RMS real error is calculated instead.

Returns

max_devi_enumpy.ndarray

maximum deviation of energy

deepmd.infer.model_devi.calc_model_devi_f(fs: ndarray, real_f: Optional[ndarray] = None, relative: Optional[float] = None, atomic: Literal[False] = False) → Tuple[ndarray, ndarray, ndarray]

deepmd.infer.model_devi.calc_model_devi_f(fs: ndarray, real_f: Optional[ndarray] = None, relative: Optional[float] = None, *, atomic: Literal[True]) → Tuple[ndarray, ndarray, ndarray, ndarray]

Calculate model deviation of force.

Parameters

fsnumpy.ndarray

size of n_models x n_frames x n_atoms x 3

real_fnumpy.ndarray or None

real force, size of n_frames x n_atoms x 3. If given, the RMS real error is calculated instead.

relativefloat, default: None

If given, calculate the relative model deviation of force. The value is the level parameter for computing the relative model deviation of the force.

atomicbool, default: False

Whether return deviation of force in all atoms

Returns

max_devi_fnumpy.ndarray

maximum deviation of force in all atoms

min_devi_fnumpy.ndarray

minimum deviation of force in all atoms

avg_devi_fnumpy.ndarray

average deviation of force in all atoms

fs_devinumpy.ndarray

deviation of force in all atoms, returned if atomic=True

deepmd.infer.model_devi.calc_model_devi_v(vs: ndarray, real_v: Optional[ndarray] = None, relative: Optional[float] = None) → Tuple[ndarray, ndarray, ndarray]

Calculate model deviation of virial.

Parameters

vsnumpy.ndarray

size of n_models x n_frames x 9

real_vnumpy.ndarray

real virial, size of n_frames x 9. If given, the RMS real error is calculated instead.

relativefloat, default: None

If given, calculate the relative model deviation of virial. The value is the level parameter for computing the relative model deviation of the virial.

Returns

max_devi_vnumpy.ndarray

maximum deviation of virial in 9 elements

min_devi_vnumpy.ndarray

minimum deviation of virial in 9 elements

avg_devi_vnumpy.ndarray

average deviation of virial in 9 elements

deepmd.infer.model_devi.make_model_devi(*, models: list, system: str, set_prefix: str, output: str, frequency: int, real_error: bool = False, atomic: bool = False, relative: Optional[float] = None, relative_v: Optional[float] = None, **kwargs)

Make model deviation calculation.

Parameters

modelslist

A list of paths of models to use for making model deviation

systemstr

The path of system to make model deviation calculation

set_prefixstr

The set prefix of the system

outputstr

The output file for model deviation results

frequencyint

The number of steps that elapse between writing coordinates in a trajectory by a MD engine (such as Gromacs / Lammps). This paramter is used to determine the index in the output file.

real_errorbool, default: False

If True, calculate the RMS real error instead of model deviation.

atomicbool, default: False

If True, calculate the force model deviation of each atom.

relativefloat, default: None

If given, calculate the relative model deviation of force. The value is the level parameter for computing the relative model deviation of the force.

relative_vfloat, default: None

If given, calculate the relative model deviation of virial. The value is the level parameter for computing the relative model deviation of the virial.

**kwargs

Arbitrary keyword arguments.

deepmd.infer.model_devi.write_model_devi_out(devi: ndarray, fname: str, header: str = '', atomic: bool = False)

Write output of model deviation.

Parameters

devinumpy.ndarray

the first column is the steps index

fnamestr

the file name to dump

headerstr, default=””

the header to dump

atomicbool, default: False

whether atomic model deviation is printed