deepmd.infer package
- class deepmd.infer.DeepPot(model_file: str, *args, **kwargs)[source]
Bases:
DeepEval
Potential energy model.
- Parameters
- model_file
Path
The name of the frozen model file.
- *args
list
Positional arguments.
- auto_batch_sizebool or
int
orAutoBatchSize
, default:True
If True, automatic batch size will be used. If int, it will be used as the initial batch size.
- neighbor_list
ase.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.
- **kwargs
dict
Keyword arguments.
- model_file
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
- coords
np.ndarray
The coordinates of the atoms, in shape (nframes, natoms, 3).
- cells
np.ndarray
The cell vectors of the system, in shape (nframes, 9). If the system is not periodic, set it to None.
- atom_types
List
[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.
- fparam
np.ndarray
,optional
The frame parameters, by default None.
- aparam
np.ndarray
,optional
The atomic parameters, by default None.
- mixed_typebool,
optional
Whether the atom_types is mixed type, by default False.
- **kwargs
Dict
[str
,Any
] Keyword arguments.
- coords
- 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
- coord
numpy.ndarray
, n_frames x n_atoms x 3 Coordinates of system to calculate
- box
numpy.ndarray
orNone
, n_frames x 3 x 3 Box to specify periodic boundary condition. If None, no pbc will be used
- atype
numpy.ndarray
, n_atoms x 1 Atom types
- models
list
of
DeepPot
models
Models used to evaluate deviation
- fname
str
orNone
File to dump results, default None
- frequency
int
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
- fparam
numpy.ndarray
frame specific parameters
- aparam
numpy.ndarray
atomic specific parameters
- real_data
dict
,optional
real data to calculate RMS real error
- atomicbool, default:
False
If True, calculate the force model deviation of each atom.
- relative
float
, 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_v
float
, 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.
- coord
- Returns
- model_devi
numpy.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.
- model_devi
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_file
Path
The name of the frozen model file.
- *args
list
Positional arguments.
- auto_batch_sizebool or
int
orAutoBatchSize
, default:True
If True, automatic batch size will be used. If int, it will be used as the initial batch size.
- neighbor_list
ase.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.
- **kwargs
dict
Keyword arguments.
- model_file
- 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.
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_file
Path
The name of the frozen model file.
- *args
list
Positional arguments.
- auto_batch_sizebool or
int
orAutoBatchSize
, default:True
If True, automatic batch size will be used. If int, it will be used as the initial batch size.
- neighbor_list
ase.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.
- **kwargs
dict
Keyword arguments.
- model_file
- 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
- coords
np.ndarray
The coordinates of the atoms, in shape (nframes, natoms, 3).
- cells
np.ndarray
The cell vectors of the system, in shape (nframes, 9). If the system is not periodic, set it to None.
- atom_types
List
[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.
- fparam
np.ndarray
,optional
The frame parameters, by default None.
- aparam
np.ndarray
,optional
The atomic parameters, by default None.
- mixed_typebool,
optional
Whether the atom_types is mixed type, by default False.
- **kwargs
Dict
[str
,Any
] Keyword arguments.
- coords
- 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_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_file
Path
The name of the frozen model file.
- *args
list
Positional arguments.
- auto_batch_sizebool or
int
orAutoBatchSize
, default:True
If True, automatic batch size will be used. If int, it will be used as the initial batch size.
- neighbor_list
ase.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.
- **kwargs
dict
Keyword arguments.
- model_file
- 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.
Evaluate output of type embedding network by using this model.
Get the number (dimension) of atomic parameters of this DP.
Get the number (dimension) of frame parameters of this DP.
Get the number of atom types of this model.
Get the number of spin atom types of this model.
get_rcut
()Get the cutoff radius of this model.
Get the selected atom types of this model.
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.
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.infer import DeepPotential >>> dp = DeepPotential("graph.pb") >>> dp.eval_typeebd()
- get_sel_type() List[int] [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.
- abstract property output_def: ModelOutputDef
Returns the output variable definitions.
- class deepmd.infer.deep_eval.DeepEvalBackend(model_file: str, *args, **kwargs)[source]
Bases:
ABC
Low-level Deep Evaluator interface.
Backends should inherbit implement this interface. High-level interface will be built on top of this.
- Parameters
- model_file
Path
The name of the frozen model file.
- *args
list
Positional arguments.
- auto_batch_sizebool or
int
orAutoBatchSize
, default:True
If True, automatic batch size will be used. If int, it will be used as the initial batch size.
- neighbor_list
ase.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.
- **kwargs
dict
Keyword arguments.
- model_file
- Attributes
model_type
The the evaluator of the model type.
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.
Evaluate output of type embedding network by using this model.
Get the number (dimension) of atomic parameters of this DP.
Get the number (dimension) of frame parameters of this DP.
Check if the model has efield.
Get the number of atom types of this model.
Get the number of spin atom types of this model.
Get the number of DOS.
get_rcut
()Get the cutoff radius of this model.
Get the selected atom types of this model.
Get the type map (element name of the atom types) of this model.
- abstract eval(coords: ndarray, cells: ndarray, atom_types: ndarray, atomic: bool = False, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, **kwargs: Dict[str, Any]) 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.
- **kwargs
Other parameters
- Returns
- output_dict
dict
The output of the evaluation. The keys are the names of the output variables, and the values are the corresponding output arrays.
- output_dict
- eval_descriptor(coords: ndarray, cells: ndarray, atom_types: ndarray, fparam: Optional[ndarray] = None, aparam: Optional[ndarray] = None, efield: 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.
- abstract get_sel_type() List[int] [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.
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_types
list
[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
- class deepmd.infer.deep_polar.DeepPolar(model_file: str, *args, **kwargs)[source]
Bases:
DeepTensor
Deep polar model.
- Parameters
- model_file
Path
The name of the frozen model file.
- *args
list
Positional arguments.
- auto_batch_sizebool or
int
orAutoBatchSize
, default:True
If True, automatic batch size will be used. If int, it will be used as the initial batch size.
- neighbor_list
ase.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.
- **kwargs
dict
Keyword arguments.
- model_file
- 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.
deepmd.infer.deep_pot module
- class deepmd.infer.deep_pot.DeepPot(model_file: str, *args, **kwargs)[source]
Bases:
DeepEval
Potential energy model.
- Parameters
- model_file
Path
The name of the frozen model file.
- *args
list
Positional arguments.
- auto_batch_sizebool or
int
orAutoBatchSize
, default:True
If True, automatic batch size will be used. If int, it will be used as the initial batch size.
- neighbor_list
ase.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.
- **kwargs
dict
Keyword arguments.
- model_file
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
- coords
np.ndarray
The coordinates of the atoms, in shape (nframes, natoms, 3).
- cells
np.ndarray
The cell vectors of the system, in shape (nframes, 9). If the system is not periodic, set it to None.
- atom_types
List
[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.
- fparam
np.ndarray
,optional
The frame parameters, by default None.
- aparam
np.ndarray
,optional
The atomic parameters, by default None.
- mixed_typebool,
optional
Whether the atom_types is mixed type, by default False.
- **kwargs
Dict
[str
,Any
] Keyword arguments.
- coords
- 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_file
Path
The name of the frozen model file.
- *args
list
Positional arguments.
- auto_batch_sizebool or
int
orAutoBatchSize
, default:True
If True, automatic batch size will be used. If int, it will be used as the initial batch size.
- neighbor_list
ase.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.
- **kwargs
dict
Keyword arguments.
- model_file
- 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_types
list
[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.
deepmd.infer.deep_wfc module
- class deepmd.infer.deep_wfc.DeepWFC(model_file: str, *args, **kwargs)[source]
Bases:
DeepTensor
Deep WFC model.
- Parameters
- model_file
Path
The name of the frozen model file.
- *args
list
Positional arguments.
- auto_batch_sizebool or
int
orAutoBatchSize
, default:True
If True, automatic batch size will be used. If int, it will be used as the initial batch size.
- neighbor_list
ase.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.
- **kwargs
dict
Keyword arguments.
- model_file
- 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.
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
- coord
numpy.ndarray
, n_frames x n_atoms x 3 Coordinates of system to calculate
- box
numpy.ndarray
orNone
, n_frames x 3 x 3 Box to specify periodic boundary condition. If None, no pbc will be used
- atype
numpy.ndarray
, n_atoms x 1 Atom types
- models
list
of
DeepPot
models
Models used to evaluate deviation
- fname
str
orNone
File to dump results, default None
- frequency
int
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
- fparam
numpy.ndarray
frame specific parameters
- aparam
numpy.ndarray
atomic specific parameters
- real_data
dict
,optional
real data to calculate RMS real error
- atomicbool, default:
False
If True, calculate the force model deviation of each atom.
- relative
float
, 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_v
float
, 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.
- coord
- Returns
- model_devi
numpy.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.
- model_devi
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
- es
numpy.ndarray
size of `n_models x n_frames x 1
- real_e
numpy.ndarray
real energy, size of n_frames x 1. If given, the RMS real error is calculated instead.
- es
- Returns
- max_devi_e
numpy.ndarray
maximum deviation of energy
- max_devi_e
- 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
- fs
numpy.ndarray
size of n_models x n_frames x n_atoms x 3
- real_f
numpy.ndarray
orNone
real force, size of n_frames x n_atoms x 3. If given, the RMS real error is calculated instead.
- relative
float
, 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
- fs
- Returns
- max_devi_f
numpy.ndarray
maximum deviation of force in all atoms
- min_devi_f
numpy.ndarray
minimum deviation of force in all atoms
- avg_devi_f
numpy.ndarray
average deviation of force in all atoms
- fs_devi
numpy.ndarray
deviation of force in all atoms, returned if atomic=True
- max_devi_f
- 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
- vs
numpy.ndarray
size of n_models x n_frames x 9
- real_v
numpy.ndarray
real virial, size of n_frames x 9. If given, the RMS real error is calculated instead.
- relative
float
, 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.
- vs
- Returns
- max_devi_v
numpy.ndarray
maximum deviation of virial in 9 elements
- min_devi_v
numpy.ndarray
minimum deviation of virial in 9 elements
- avg_devi_v
numpy.ndarray
average deviation of virial in 9 elements
- max_devi_v
- 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
- models
list
A list of paths of models to use for making model deviation
- system
str
The path of system to make model deviation calculation
- set_prefix
str
The set prefix of the system
- output
str
The output file for model deviation results
- frequency
int
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.
- relative
float
, 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_v
float
, 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.
- models
- deepmd.infer.model_devi.write_model_devi_out(devi: ndarray, fname: str, header: str = '', atomic: bool = False)[source]
Write output of model deviation.
- Parameters
- devi
numpy.ndarray
the first column is the steps index
- fname
str
the file name to dump
- header
str
, default=”” the header to dump
- atomicbool, default:
False
whether atomic model deviation is printed
- devi