deepmd.tf.infer package

Submodule containing all the implemented potentials.

class deepmd.tf.infer.DeepDOS(model_file: str, *args, **kwargs)[source]

Bases: DeepEval

Deep density of states model.

Parameters

model_filePath: The name of the frozen model file.
*argslist: Positional arguments.
auto_batch_sizebool or int or AutoBatchSize, default: True: If True, automatic batch size will be used. If int, it will be used as the initial batch size.
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.
**kwargsdict: Keyword arguments.

Attributes

has_efield: Check if the model has efield.
output_def: Get the output definition of this model.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate energy, force, and virial.
`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_ntypes_spin`()	Get the number of spin atom types of this model.
`get_rcut`()	Get the cutoff 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.

get_numb_dos

eval(coords: ndarray, cells: Optional[ndarray], atom_types: Union[List[int], ndarray], atomic: bool = False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, mixed_type: bool = False, **kwargs: Dict[str, Any]) → Tuple[ndarray, ...][source]

Evaluate energy, force, and virial. If atomic is True, also return atomic energy and atomic virial.

Parameters

coordsnp.ndarray: The coordinates of the atoms, in shape (nframes, natoms, 3).
cellsnp.ndarray: The cell vectors of the system, in shape (nframes, 9). If the system is not periodic, set it to None.
atom_typesList[int] or np.ndarray: The types of the atoms. If mixed_type is False, the shape is (natoms,); otherwise, the shape is (nframes, natoms).
atomicbool, optional: Whether to return atomic energy and atomic virial, by default False.
fparamnp.ndarray, optional: The frame parameters, by default None.
aparamnp.ndarray, optional: The atomic parameters, by default None.
mixed_typebool, optional: Whether the atom_types is mixed type, by default False.
**kwargsDict[str, Any]: Keyword arguments.

Returns

energy: The energy of the system, in shape (nframes,).
force: The force of the system, in shape (nframes, natoms, 3).
virial: The virial of the system, in shape (nframes, 9).
atomic_energy: The atomic energy of the system, in shape (nframes, natoms). Only returned when atomic is True.
atomic_virial: The atomic virial of the system, in shape (nframes, natoms, 9). Only returned when atomic is True.

get_numb_dos() → int[source]

property output_def: ModelOutputDef: Get the output definition of this model.

class deepmd.tf.infer.DeepDipole(model_file: str, *args, **kwargs)[source]

Bases: DeepTensor

Deep dipole model.

Parameters

model_filePath: The name of the frozen model file.
*argslist: Positional arguments.
auto_batch_sizebool or int or AutoBatchSize, default: True: If True, automatic batch size will be used. If int, it will be used as the initial batch size.
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.
**kwargsdict: Keyword arguments.

Attributes

has_efield: Check if the model has efield.
output_def: Get the output definition of this model.
output_tensor_name: The name of the tensor.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_descriptor`(coords, cells, atom_types[, ...])	Evaluate descriptors by using this DP.
`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_ntypes_spin`()	Get the number of spin atom types of this model.
`get_rcut`()	Get the cutoff 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.

property output_tensor_name: str: The name of the tensor.

class deepmd.tf.infer.DeepEval(model_file: str, *args, **kwargs)[source]

Bases: DeepEvalBackend

TensorFlow backend implementation for DeepEval.

Parameters

model_filePath: The name of the frozen model file.
output_defModelOutputDef: The output definition of the model.
*argslist: Positional arguments.
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.
**kwargsdict: Keyword arguments.

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_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_has_efield`()	Check if the model has efield.
`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_numb_dos`()	Get the number of DOS.
`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)	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)[source]

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(coords: ndarray, cells: ndarray, atom_types: ndarray, atomic: bool = False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, efield: Optional[ndarray] = None) → Dict[str, ndarray][source]

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

Returns

output_dictdict: The output of the evaluation. The keys are the names of the output variables, and the values are the corresponding output arrays.

eval_descriptor(coords: ndarray, cells: ndarray, atom_types: ndarray, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, efield: Optional[ndarray] = None) → ndarray[source]

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

Returns

descriptor: Descriptors.

eval_typeebd() → ndarray[source]

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.tf.utils.type_embed.TypeEmbedNet: The type embedding network.

Examples

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

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

get_dim_aparam() → int[source]: Get the number (dimension) of atomic parameters of this DP.

get_dim_fparam() → int[source]: Get the number (dimension) of frame parameters of this DP.

get_has_efield() → bool[source]: Check if the model has efield.

get_ntypes() → int[source]: Get the number of atom types of this model.

get_ntypes_spin() → int[source]: Get the number of spin atom types of this model.

get_numb_dos() → int[source]: Get the number of DOS.

get_rcut() → float[source]: Get the cut-off radius of this model.

get_sel_type() → Optional[ndarray][source]

Get the selected atom types of this model.

Only atoms with selected atom types have atomic contribution to the result of the model. If returning an empty list, all atom types are selected.

get_type_map() → List[str][source]: Get the type map (element name of the atom types) of this model.

make_natoms_vec(atom_types: ndarray) → ndarray[source]

Make the natom vector used by deepmd-kit.

Parameters

atom_types: The type of atoms

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: DeepEvalWrapper

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[source]

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)[source]

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

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.tf.infer.DeepGlobalPolar(model_file: str, *args, **kwargs)[source]

Bases: DeepTensor

Attributes

has_efield: Check if the model has efield.
output_def: Get the output definition of this model.
output_tensor_name: The name of the tensor.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_descriptor`(coords, cells, atom_types[, ...])	Evaluate descriptors by using this DP.
`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_ntypes_spin`()	Get the number of spin atom types of this model.
`get_rcut`()	Get the cutoff 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.

eval(coords: ndarray, cells: Optional[ndarray], atom_types: Union[List[int], ndarray], atomic: bool = False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, mixed_type: bool = False, **kwargs: dict) → ndarray[source]

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_typeslist[int] or np.ndarray: 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
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

property output_tensor_name: str: The name of the tensor.

class deepmd.tf.infer.DeepPolar(model_file: str, *args, **kwargs)[source]

Bases: DeepTensor

Deep polar model.

Parameters

model_filePath: The name of the frozen model file.
*argslist: Positional arguments.
auto_batch_sizebool or int or AutoBatchSize, default: True: If True, automatic batch size will be used. If int, it will be used as the initial batch size.
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.
**kwargsdict: Keyword arguments.

Attributes

has_efield: Check if the model has efield.
output_def: Get the output definition of this model.
output_tensor_name: The name of the tensor.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_descriptor`(coords, cells, atom_types[, ...])	Evaluate descriptors by using this DP.
`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_ntypes_spin`()	Get the number of spin atom types of this model.
`get_rcut`()	Get the cutoff 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.

property output_tensor_name: str: The name of the tensor.

class deepmd.tf.infer.DeepPot(model_file: str, *args, **kwargs)[source]

Bases: DeepEval

Potential energy model.

Parameters

model_filePath: The name of the frozen model file.
*argslist: Positional arguments.
auto_batch_sizebool or int or AutoBatchSize, default: True: If True, automatic batch size will be used. If int, it will be used as the initial batch size.
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.
**kwargsdict: Keyword arguments.

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

has_efield: Check if the model has efield.
output_def: Get the output definition of this model.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate energy, force, and virial.
`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_ntypes_spin`()	Get the number of spin atom types of this model.
`get_rcut`()	Get the cutoff 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.

eval(coords: ndarray, cells: Optional[ndarray], atom_types: Union[List[int], ndarray], atomic: bool = False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, mixed_type: bool = False, **kwargs: Dict[str, Any]) → Tuple[ndarray, ...][source]

Evaluate energy, force, and virial. If atomic is True, also return atomic energy and atomic virial.

Parameters

coordsnp.ndarray: The coordinates of the atoms, in shape (nframes, natoms, 3).
cellsnp.ndarray: The cell vectors of the system, in shape (nframes, 9). If the system is not periodic, set it to None.
atom_typesList[int] or np.ndarray: The types of the atoms. If mixed_type is False, the shape is (natoms,); otherwise, the shape is (nframes, natoms).
atomicbool, optional: Whether to return atomic energy and atomic virial, by default False.
fparamnp.ndarray, optional: The frame parameters, by default None.
aparamnp.ndarray, optional: The atomic parameters, by default None.
mixed_typebool, optional: Whether the atom_types is mixed type, by default False.
**kwargsDict[str, Any]: Keyword arguments.

Returns

energy: The energy of the system, in shape (nframes,).
force: The force of the system, in shape (nframes, natoms, 3).
virial: The virial of the system, in shape (nframes, 9).
atomic_energy: The atomic energy of the system, in shape (nframes, natoms). Only returned when atomic is True.
atomic_virial: The atomic virial of the system, in shape (nframes, natoms, 9). Only returned when atomic is True.

property output_def: ModelOutputDef: Get the output definition of this model.

deepmd.tf.infer.DeepPotential(*args, **kwargs) → DeepEval[source]

Factory function that forwards to DeepEval (for compatbility).

Parameters

*args: positional arguments
**kwargs: keyword arguments

Returns

DeepEval: potentials

class deepmd.tf.infer.DeepWFC(model_file: str, *args, **kwargs)[source]

Bases: DeepTensor

Deep WFC model.

Parameters

model_filePath: The name of the frozen model file.
*argslist: Positional arguments.
auto_batch_sizebool or int or AutoBatchSize, default: True: If True, automatic batch size will be used. If int, it will be used as the initial batch size.
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.
**kwargsdict: Keyword arguments.

Attributes

has_efield: Check if the model has efield.
output_def: Get the output definition of this model.
output_tensor_name: The name of the tensor.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_descriptor`(coords, cells, atom_types[, ...])	Evaluate descriptors by using this DP.
`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_ntypes_spin`()	Get the number of spin atom types of this model.
`get_rcut`()	Get the cutoff 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.

property output_tensor_name: str: The name of the tensor.

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

Bases: DeepDipoleOld

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[source]: 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][source]

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[source]

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.tf.infer.EwaldRecp(hh, beta)[source]

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][source]

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.tf.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)[source]

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.tf.infer import calc_model_devi
>>> from deepmd.tf.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.tf.infer.data_modifier module

class deepmd.tf.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)[source]

Bases: DeepDipoleOld

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[source]: 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][source]

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[source]

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.tf.infer.deep_dipole module

class deepmd.tf.infer.deep_dipole.DeepDipole(model_file: str, *args, **kwargs)[source]

Bases: DeepTensor

Deep dipole model.

Parameters

model_filePath: The name of the frozen model file.
*argslist: Positional arguments.
auto_batch_sizebool or int or AutoBatchSize, default: True: If True, automatic batch size will be used. If int, it will be used as the initial batch size.
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.
**kwargsdict: Keyword arguments.

Attributes

has_efield: Check if the model has efield.
output_def: Get the output definition of this model.
output_tensor_name: The name of the tensor.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_descriptor`(coords, cells, atom_types[, ...])	Evaluate descriptors by using this DP.
`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_ntypes_spin`()	Get the number of spin atom types of this model.
`get_rcut`()	Get the cutoff 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.

property output_tensor_name: str: The name of the tensor.

deepmd.tf.infer.deep_dos module

class deepmd.tf.infer.deep_dos.DeepDOS(model_file: str, *args, **kwargs)[source]

Bases: DeepEval

Deep density of states model.

Parameters

model_filePath: The name of the frozen model file.
*argslist: Positional arguments.
auto_batch_sizebool or int or AutoBatchSize, default: True: If True, automatic batch size will be used. If int, it will be used as the initial batch size.
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.
**kwargsdict: Keyword arguments.

Attributes

has_efield: Check if the model has efield.
output_def: Get the output definition of this model.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate energy, force, and virial.
`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_ntypes_spin`()	Get the number of spin atom types of this model.
`get_rcut`()	Get the cutoff 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.

get_numb_dos

eval(coords: ndarray, cells: Optional[ndarray], atom_types: Union[List[int], ndarray], atomic: bool = False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, mixed_type: bool = False, **kwargs: Dict[str, Any]) → Tuple[ndarray, ...][source]

Evaluate energy, force, and virial. If atomic is True, also return atomic energy and atomic virial.

Parameters

coordsnp.ndarray: The coordinates of the atoms, in shape (nframes, natoms, 3).
cellsnp.ndarray: The cell vectors of the system, in shape (nframes, 9). If the system is not periodic, set it to None.
atom_typesList[int] or np.ndarray: The types of the atoms. If mixed_type is False, the shape is (natoms,); otherwise, the shape is (nframes, natoms).
atomicbool, optional: Whether to return atomic energy and atomic virial, by default False.
fparamnp.ndarray, optional: The frame parameters, by default None.
aparamnp.ndarray, optional: The atomic parameters, by default None.
mixed_typebool, optional: Whether the atom_types is mixed type, by default False.
**kwargsDict[str, Any]: Keyword arguments.

Returns

energy: The energy of the system, in shape (nframes,).
force: The force of the system, in shape (nframes, natoms, 3).
virial: The virial of the system, in shape (nframes, 9).
atomic_energy: The atomic energy of the system, in shape (nframes, natoms). Only returned when atomic is True.
atomic_virial: The atomic virial of the system, in shape (nframes, natoms, 9). Only returned when atomic is True.

get_numb_dos() → int[source]

property output_def: ModelOutputDef: Get the output definition of this model.

deepmd.tf.infer.deep_eval module

class deepmd.tf.infer.deep_eval.DeepEval(model_file: str, *args, **kwargs)[source]

Bases: DeepEvalBackend

TensorFlow backend implementation for DeepEval.

Parameters

model_filePath: The name of the frozen model file.
output_defModelOutputDef: The output definition of the model.
*argslist: Positional arguments.
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.
**kwargsdict: Keyword arguments.

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_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_has_efield`()	Check if the model has efield.
`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_numb_dos`()	Get the number of DOS.
`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)	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)[source]

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(coords: ndarray, cells: ndarray, atom_types: ndarray, atomic: bool = False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, efield: Optional[ndarray] = None) → Dict[str, ndarray][source]

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

Returns

output_dictdict: The output of the evaluation. The keys are the names of the output variables, and the values are the corresponding output arrays.

eval_descriptor(coords: ndarray, cells: ndarray, atom_types: ndarray, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, efield: Optional[ndarray] = None) → ndarray[source]

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

Returns

descriptor: Descriptors.

eval_typeebd() → ndarray[source]

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.

deepmd.tf.infer.deep_polar module

class deepmd.tf.infer.deep_polar.DeepGlobalPolar(model_file: str, *args, **kwargs)[source]

Bases: DeepTensor

Attributes

has_efield: Check if the model has efield.
output_def: Get the output definition of this model.
output_tensor_name: The name of the tensor.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_descriptor`(coords, cells, atom_types[, ...])	Evaluate descriptors by using this DP.
`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_ntypes_spin`()	Get the number of spin atom types of this model.
`get_rcut`()	Get the cutoff 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.

eval(coords: ndarray, cells: Optional[ndarray], atom_types: Union[List[int], ndarray], atomic: bool = False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, mixed_type: bool = False, **kwargs: dict) → ndarray[source]

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_typeslist[int] or np.ndarray: 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
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

property output_tensor_name: str: The name of the tensor.

class deepmd.tf.infer.deep_polar.DeepPolar(model_file: str, *args, **kwargs)[source]

Bases: DeepTensor

Deep polar model.

Parameters

model_filePath: The name of the frozen model file.
*argslist: Positional arguments.
auto_batch_sizebool or int or AutoBatchSize, default: True: If True, automatic batch size will be used. If int, it will be used as the initial batch size.
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.
**kwargsdict: Keyword arguments.

Attributes

has_efield: Check if the model has efield.
output_def: Get the output definition of this model.
output_tensor_name: The name of the tensor.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_descriptor`(coords, cells, atom_types[, ...])	Evaluate descriptors by using this DP.
`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_ntypes_spin`()	Get the number of spin atom types of this model.
`get_rcut`()	Get the cutoff 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.

property output_tensor_name: str: The name of the tensor.

deepmd.tf.infer.deep_pot module

class deepmd.tf.infer.deep_pot.DeepPot(model_file: str, *args, **kwargs)[source]

Bases: DeepEval

Potential energy model.

Parameters

model_filePath: The name of the frozen model file.
*argslist: Positional arguments.
auto_batch_sizebool or int or AutoBatchSize, default: True: If True, automatic batch size will be used. If int, it will be used as the initial batch size.
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.
**kwargsdict: Keyword arguments.

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

has_efield: Check if the model has efield.
output_def: Get the output definition of this model.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate energy, force, and virial.
`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_ntypes_spin`()	Get the number of spin atom types of this model.
`get_rcut`()	Get the cutoff 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.

eval(coords: ndarray, cells: Optional[ndarray], atom_types: Union[List[int], ndarray], atomic: bool = False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, mixed_type: bool = False, **kwargs: Dict[str, Any]) → Tuple[ndarray, ...][source]

Evaluate energy, force, and virial. If atomic is True, also return atomic energy and atomic virial.

Parameters

coordsnp.ndarray: The coordinates of the atoms, in shape (nframes, natoms, 3).
cellsnp.ndarray: The cell vectors of the system, in shape (nframes, 9). If the system is not periodic, set it to None.
atom_typesList[int] or np.ndarray: The types of the atoms. If mixed_type is False, the shape is (natoms,); otherwise, the shape is (nframes, natoms).
atomicbool, optional: Whether to return atomic energy and atomic virial, by default False.
fparamnp.ndarray, optional: The frame parameters, by default None.
aparamnp.ndarray, optional: The atomic parameters, by default None.
mixed_typebool, optional: Whether the atom_types is mixed type, by default False.
**kwargsDict[str, Any]: Keyword arguments.

Returns

energy: The energy of the system, in shape (nframes,).
force: The force of the system, in shape (nframes, natoms, 3).
virial: The virial of the system, in shape (nframes, 9).
atomic_energy: The atomic energy of the system, in shape (nframes, natoms). Only returned when atomic is True.
atomic_virial: The atomic virial of the system, in shape (nframes, natoms, 9). Only returned when atomic is True.

property output_def: ModelOutputDef: Get the output definition of this model.

deepmd.tf.infer.deep_tensor module

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

Bases: DeepEvalOld

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[source]

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, ...][source]

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[source]: Get the number (dimension) of atomic parameters of this DP.

get_dim_fparam() → int[source]: Get the number (dimension) of frame parameters of this DP.

get_ntypes() → int[source]: Get the number of atom types of this model.

get_rcut() → float[source]: Get the cut-off radius of this model.

get_sel_type() → List[int][source]: Get the selected atom types of this model.

get_type_map() → List[str][source]: 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.tf.infer.deep_wfc module

class deepmd.tf.infer.deep_wfc.DeepWFC(model_file: str, *args, **kwargs)[source]

Bases: DeepTensor

Deep WFC model.

Parameters

model_filePath: The name of the frozen model file.
*argslist: Positional arguments.
auto_batch_sizebool or int or AutoBatchSize, default: True: If True, automatic batch size will be used. If int, it will be used as the initial batch size.
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.
**kwargsdict: Keyword arguments.

Attributes

has_efield: Check if the model has efield.
output_def: Get the output definition of this model.
output_tensor_name: The name of the tensor.

Methods

`eval`(coords, cells, atom_types[, atomic, ...])	Evaluate the model.
`eval_descriptor`(coords, cells, atom_types[, ...])	Evaluate descriptors by using this DP.
`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_ntypes_spin`()	Get the number of spin atom types of this model.
`get_rcut`()	Get the cutoff 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.

property output_tensor_name: str: The name of the tensor.

deepmd.tf.infer.ewald_recp module

class deepmd.tf.infer.ewald_recp.EwaldRecp(hh, beta)[source]

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][source]

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.tf.infer.model_devi module

deepmd.tf.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)[source]

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.tf.infer import calc_model_devi
>>> from deepmd.tf.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.tf.infer.model_devi.calc_model_devi_e(es: ndarray, real_e: Optional[ndarray] = None) → ndarray[source]

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.tf.infer.model_devi.calc_model_devi_f(fs: ndarray, real_f: Optional[ndarray] = None, relative: Optional[float] = None, atomic: bool = False) → Tuple[ndarray, ...][source]

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

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.tf.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)[source]

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.tf.infer.model_devi.write_model_devi_out(devi: ndarray, fname: str, header: str = '', atomic: bool = False)[source]

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