deepmd.infer package

Submodule containing all the implemented potentials.

class deepmd.infer.DeepDipole(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: DeepTensor

Constructor.

Parameters:

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Warning

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

Attributes:

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

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

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

class deepmd.infer.DeepEval(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: bool | int | AutoBatchSize = False)[source]

Bases: object

Common methods for DeepPot, DeepWFC, DeepPolar, …

Parameters:

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation
auto_batch_sizebool or int or AutomaticBatchSize, default: False: If True, automatic batch size will be used. If int, it will be used as the initial batch size.

Attributes:

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

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

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

Examples

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

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

load_prefix: str

make_natoms_vec(atom_types: ndarray, mixed_type: bool = False) → ndarray[source]

Make the natom vector used by deepmd-kit.

Parameters:

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

Returns:

natoms: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms

property model_type: str

Get type of model.

:type:str

property model_version: str

Get version of model.

Returns:

str: version of model

static reverse_map(vec: ndarray, imap: List[int]) → ndarray[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: List[int] | None = None, mixed_type: bool = False)[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
mixed_type: Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns:

coord_out: The coordinates after sorting
atom_type_out: The atom types after sorting
idx_map: The index mapping from the input to the output. For example coord_out = coord[:,idx_map,:]
sel_atom_type: Only output if sel_atoms is not None The sorted selected atom types
sel_idx_map: Only output if sel_atoms is not None The index mapping from the selected atoms to sorted selected atoms.

class deepmd.infer.DeepGlobalPolar(model_file: str, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: DeepTensor

Constructor.

Parameters:

model_filestr: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Attributes:

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

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

eval(coords: ndarray, cells: ndarray, atom_types: List[int], atomic: bool = False, fparam: ndarray | None = None, aparam: ndarray | None = None, efield: ndarray | None = None) → 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: Not used in this model
fparam: Not used in this model
aparam: Not used in this model
efield: Not used in this model

Returns:

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

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

class deepmd.infer.DeepPolar(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: DeepTensor

Constructor.

Parameters:

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Warning

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

Attributes:

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

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

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

class deepmd.infer.DeepPot(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: bool | int | AutoBatchSize = True)[source]

Bases: DeepEval

Constructor.

Parameters:

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation
auto_batch_sizebool or int or AutomaticBatchSize, default: True: If True, automatic batch size will be used. If int, it will be used as the initial batch size.

Warning

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

Examples

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

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

Attributes:

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`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_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Unsupported in this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map.
`sort_input`(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

eval(coords: ndarray, cells: ndarray, atom_types: List[int], atomic: bool = False, fparam: ndarray | None = None, aparam: ndarray | None = None, efield: ndarray | None = None, mixed_type: bool = False) → Tuple[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
mixed_type: Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns:

energy: The system energy.
force: The force on each atom
virial: The virial
atom_energy: The atomic energy. Only returned when atomic == True
atom_virial: The atomic virial. Only returned when atomic == True

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

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]: Unsupported in this model.

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

load_prefix: str

Factory function that will inialize appropriate potential read from model_file.

Parameters:

model_filestr: The name of the frozen model file.
load_prefixstr: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Returns:

Union[DeepDipole, DeepGlobalPolar, DeepPolar, DeepPot, DeepWFC]: one of the available potentials

Raises:

RuntimeError: if model file does not correspond to any implementd potential

class deepmd.infer.DeepWFC(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: DeepTensor

Constructor.

Parameters:

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Warning

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

Attributes:

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

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

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

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

Bases: DeepDipole

Parameters:

model_name: The model file for the DeepDipole model
model_charge_map: Gives the amount of charge for the wfcc
sys_charge_map: Gives the amount of charge for the real atoms
ewald_h: Grid spacing of the reciprocal part of Ewald sum. Unit: A
ewald_beta: Splitting parameter of the Ewald sum. Unit: A^{-1}

Attributes:

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`build_fv_graph`()	Build the computational graph for the force and virial inference.
`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)	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

load_prefix: str

modify_data(data: dict) → 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

class deepmd.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.infer.calc_model_devi(coord, box, atype, models, fname=None, frequency=1)[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

Returns:

model_devinumpy.ndarray, n_frames x 7: Model deviation results. The first column is index of steps, the other 6 columns are max_devi_v, min_devi_v, avg_devi_v, max_devi_f, min_devi_f, avg_devi_f.

Examples

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

Submodules

deepmd.infer.data_modifier module

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

Bases: DeepDipole

Parameters:

model_name: The model file for the DeepDipole model
model_charge_map: Gives the amount of charge for the wfcc
sys_charge_map: Gives the amount of charge for the real atoms
ewald_h: Grid spacing of the reciprocal part of Ewald sum. Unit: A
ewald_beta: Splitting parameter of the Ewald sum. Unit: A^{-1}

Attributes:

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`build_fv_graph`()	Build the computational graph for the force and virial inference.
`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)	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

load_prefix: str

modify_data(data: dict) → 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

deepmd.infer.deep_dipole module

class deepmd.infer.deep_dipole.DeepDipole(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: DeepTensor

Constructor.

Parameters:

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Warning

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

Attributes:

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

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

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

deepmd.infer.deep_eval module

class deepmd.infer.deep_eval.DeepEval(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: bool | int | AutoBatchSize = False)[source]

Bases: object

Common methods for DeepPot, DeepWFC, DeepPolar, …

Parameters:

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation
auto_batch_sizebool or int or AutomaticBatchSize, default: False: If True, automatic batch size will be used. If int, it will be used as the initial batch size.

Attributes:

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

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

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, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: DeepTensor

Constructor.

Parameters:

model_filestr: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Attributes:

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

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

eval(coords: ndarray, cells: ndarray, atom_types: List[int], atomic: bool = False, fparam: ndarray | None = None, aparam: ndarray | None = None, efield: ndarray | None = None) → 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: Not used in this model
fparam: Not used in this model
aparam: Not used in this model
efield: Not used in this model

Returns:

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

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

class deepmd.infer.deep_polar.DeepPolar(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: DeepTensor

Constructor.

Parameters:

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Warning

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

Attributes:

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

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

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

deepmd.infer.deep_pot module

class deepmd.infer.deep_pot.DeepPot(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: bool | int | AutoBatchSize = True)[source]

Bases: DeepEval

Constructor.

Parameters:

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation
auto_batch_sizebool or int or AutomaticBatchSize, default: True: If True, automatic batch size will be used. If int, it will be used as the initial batch size.

Warning

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

Examples

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

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

Attributes:

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`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_ntypes`()	Get the number of atom types of this model.
`get_rcut`()	Get the cut-off radius of this model.
`get_sel_type`()	Unsupported in this model.
`get_type_map`()	Get the type map (element name of the atom types) of this model.
`make_natoms_vec`(atom_types[, mixed_type])	Make the natom vector used by deepmd-kit.
`reverse_map`(vec, imap)	Reverse mapping of a vector according to the index map.
`sort_input`(coord, atom_type[, sel_atoms, ...])	Sort atoms in the system according their types.

eval(coords: ndarray, cells: ndarray, atom_types: List[int], atomic: bool = False, fparam: ndarray | None = None, aparam: ndarray | None = None, efield: ndarray | None = None, mixed_type: bool = False) → Tuple[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
mixed_type: Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns:

energy: The system energy.
force: The force on each atom
virial: The virial
atom_energy: The atomic energy. Only returned when atomic == True
atom_virial: The atomic virial. Only returned when atomic == True

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

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]: Unsupported in this model.

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

load_prefix: str

deepmd.infer.deep_tensor module

class deepmd.infer.deep_tensor.DeepTensor(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: DeepEval

Evaluates a tensor model.

Parameters:

model_file: str: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Attributes:

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

`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: ndarray | None = None, aparam: ndarray | None = None, efield: ndarray | None = 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: array | None = None, aparam: array | None = None, efield: array | None = 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.

load_prefix: str

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

deepmd.infer.deep_wfc module

class deepmd.infer.deep_wfc.DeepWFC(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]

Bases: DeepTensor

Constructor.

Parameters:

model_filePath: The name of the frozen model file.
load_prefix: str: The prefix in the load computational graph
default_tf_graphbool: If uses the default tf graph, otherwise build a new tf graph for evaluation

Warning

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

Attributes:

model_type: Get type of model.
model_version: Get version of model.
sess: Get TF session.

Methods

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

get_dim_aparam() → int[source]: Unsupported in this model.

get_dim_fparam() → int[source]: Unsupported in this model.

load_prefix: str

deepmd.infer.ewald_recp module

class deepmd.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.infer.model_devi module

deepmd.infer.model_devi.calc_model_devi(coord, box, atype, models, fname=None, frequency=1)[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

Returns:

model_devinumpy.ndarray, n_frames x 7: Model deviation results. The first column is index of steps, the other 6 columns are max_devi_v, min_devi_v, avg_devi_v, max_devi_f, min_devi_f, avg_devi_f.

Examples

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

deepmd.infer.model_devi.calc_model_devi_e(es: ndarray)[source]

Parameters:

esnumpy.ndarray: size of `n_models x n_frames x n_atoms

deepmd.infer.model_devi.calc_model_devi_f(fs: ndarray)[source]

Parameters:

fsnumpy.ndarray: size of n_models x n_frames x n_atoms x 3

deepmd.infer.model_devi.calc_model_devi_v(vs: ndarray)[source]

Parameters:

vsnumpy.ndarray: size of n_models x n_frames x 9

deepmd.infer.model_devi.make_model_devi(*, models: list, system: str, set_prefix: str, output: str, frequency: int, **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.
**kwargs: Arbitrary keyword arguments.

deepmd.infer.model_devi.write_model_devi_out(devi: ndarray, fname: str, header: str = '')[source]

Parameters:

devinumpy.ndarray: the first column is the steps index
fnamestr: the file name to dump
headerstr, default=””: the header to dump