deepmd.infer package

class deepmd.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.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.infer.deep_dipole module

class deepmd.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.infer.deep_dos module

class deepmd.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.infer.deep_eval module

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

Bases: ABC

High-level Deep Evaluator interface.

The specific DeepEval, such as DeepPot and DeepTensor, should inherit from this class. This class provides a high-level interface on the top of the low-level interface.

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: Returns the output variable definitions.

Methods

`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_descriptor(coords: ndarray, cells: Optional[ndarray], atom_types: ndarray, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, mixed_type: bool = False, **kwargs: Dict[str, Any]) → 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
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.

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.infer.deep_polar module

class deepmd.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.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.infer.deep_pot module

class deepmd.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.infer.deep_tensor module

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

Bases: DeepEval

Deep Tensor 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.

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

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

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

deepmd.infer.deep_wfc module

class deepmd.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.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)[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.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.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][source]

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][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.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.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