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:
deepmd.infer.deep_tensor.DeepTensor
Constructor.
- Parameters
- model_file
Path
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
- model_file
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.
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.
Unsupported in this model.
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)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.
- class deepmd.infer.DeepEval(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: Union[bool, int, deepmd.utils.batch_size.AutoBatchSize] = False)[source]
Bases:
object
Common methods for DeepPot, DeepWFC, DeepPolar, …
- Parameters
- model_file
Path
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
orAutomaticBatchSize
, default:False
If True, automatic batch size will be used. If int, it will be used as the initial batch size.
- model_file
- Attributes
model_type
Get type of model.
model_version
Get version of model.
Methods
make_natoms_vec
(atom_types)Make the natom vector used by deepmd-kit.
reverse_map
(vec, imap)Reverse mapping of a vector according to the index map
sort_input
(coord, atom_type[, sel_atoms])Sort atoms in the system according their types.
- make_natoms_vec(atom_types: numpy.ndarray) numpy.ndarray [source]
Make the natom vector used by deepmd-kit.
- Parameters
- atom_types
The type of atoms
- Returns
natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- static reverse_map(vec: numpy.ndarray, imap: List[int]) numpy.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.
- static sort_input(coord: numpy.ndarray, atom_type: numpy.ndarray, sel_atoms: Optional[List[int]] = None)[source]
Sort atoms in the system according their types.
- Parameters
- coord
The coordinates of atoms. Should be of shape [nframes, natoms, 3]
- atom_type
The type of atoms Should be of shape [natoms]
- sel_atom
The selected atoms by type
- Returns
coord_out
The coordinates after sorting
atom_type_out
The atom types after sorting
idx_map
The index mapping from the input to the output. For example coord_out = coord[:,idx_map,:]
sel_atom_type
Only output if sel_atoms is not None The sorted selected atom types
sel_idx_map
Only output if sel_atoms is not None The index mapping from the selected atoms to sorted selected atoms.
- class deepmd.infer.DeepGlobalPolar(model_file: str, load_prefix: str = 'load', default_tf_graph: bool = False)[source]
Bases:
deepmd.infer.deep_tensor.DeepTensor
Constructor.
- Parameters
- Attributes
model_type
Get type of model.
model_version
Get version of model.
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.
Unsupported in this model.
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)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: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[numpy.ndarray] = None, aparam: Optional[numpy.ndarray] = None, efield: Optional[numpy.ndarray] = None) numpy.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
- class deepmd.infer.DeepPolar(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]
Bases:
deepmd.infer.deep_tensor.DeepTensor
Constructor.
- Parameters
- model_file
Path
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
- model_file
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.
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.
Unsupported in this model.
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)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.
- class deepmd.infer.DeepPot(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: Union[bool, int, deepmd.utils.batch_size.AutoBatchSize] = True)[source]
Bases:
deepmd.infer.deep_eval.DeepEval
Constructor.
- Parameters
- model_file
Path
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
orAutomaticBatchSize
, default:True
If True, automatic batch size will be used. If int, it will be used as the initial batch size.
- model_file
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.
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.
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_rcut
()Get the cut-off radius of this model.
Unsupported in this model.
Get the type map (element name of the atom types) of this model.
make_natoms_vec
(atom_types)Make the natom vector used by deepmd-kit.
reverse_map
(vec, imap)Reverse mapping of a vector according to the index map
sort_input
(coord, atom_type[, sel_atoms])Sort atoms in the system according their types.
- eval(coords: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[numpy.ndarray] = None, aparam: Optional[numpy.ndarray] = None, efield: Optional[numpy.ndarray] = None) Tuple[numpy.ndarray, ...] [source]
Evaluate the energy, force and virial by using this DP.
- Parameters
- coords
The coordinates of atoms. The array should be of size nframes x natoms x 3
- cells
The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9
- atom_types
The atom types The list should contain natoms ints
- atomic
Calculate the atomic energy and virial
- fparam
The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.
- aparam
The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.
- efield
The external field on atoms. The array should be of size nframes x natoms x 3
- Returns
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: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], fparam: Optional[numpy.ndarray] = None, aparam: Optional[numpy.ndarray] = None, efield: Optional[numpy.ndarray] = None) numpy.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
- Returns
descriptor
Descriptors.
- deepmd.infer.DeepPotential(model_file: Union[str, pathlib.Path], load_prefix: str = 'load', default_tf_graph: bool = False) Union[deepmd.infer.deep_dipole.DeepDipole, deepmd.infer.deep_polar.DeepGlobalPolar, deepmd.infer.deep_polar.DeepPolar, deepmd.infer.deep_pot.DeepPot, deepmd.infer.deep_wfc.DeepWFC] [source]
Factory function that will inialize appropriate potential read from model_file.
- 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
- 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:
deepmd.infer.deep_tensor.DeepTensor
Constructor.
- Parameters
- model_file
Path
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
- model_file
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.
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.
Unsupported in this model.
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)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.
- 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:
deepmd.infer.deep_dipole.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.
Methods
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.
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)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() tensorflow.python.framework.ops.Tensor [source]
Build the computational graph for the force and virial inference.
- eval(coord: numpy.ndarray, box: numpy.ndarray, atype: numpy.ndarray, eval_fv: bool = True) Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray] [source]
Evaluate the modification
- Parameters
- coord
The coordinates of atoms
- box
The simulation region. PBC is assumed
- atype
The atom types
- eval_fv
Evaluate force and virial
- Returns
tot_e
The energy modification
tot_f
The force modification
tot_v
The virial modification
- modify_data(data: dict) 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: numpy.ndarray, charge: numpy.ndarray, box: numpy.ndarray) Tuple[numpy.ndarray, numpy.ndarray, numpy.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, nopbc=True)[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
- nopbcbool
Whether to use pbc conditions
- coord
- Returns
- model_devi
numpy.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.
- model_devi
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:
deepmd.infer.deep_dipole.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.
Methods
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.
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)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() tensorflow.python.framework.ops.Tensor [source]
Build the computational graph for the force and virial inference.
- eval(coord: numpy.ndarray, box: numpy.ndarray, atype: numpy.ndarray, eval_fv: bool = True) Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray] [source]
Evaluate the modification
- Parameters
- coord
The coordinates of atoms
- box
The simulation region. PBC is assumed
- atype
The atom types
- eval_fv
Evaluate force and virial
- Returns
tot_e
The energy modification
tot_f
The force modification
tot_v
The virial modification
- modify_data(data: dict) 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:
deepmd.infer.deep_tensor.DeepTensor
Constructor.
- Parameters
- model_file
Path
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
- model_file
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.
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.
Unsupported in this model.
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)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.
deepmd.infer.deep_eval module
- class deepmd.infer.deep_eval.DeepEval(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: Union[bool, int, deepmd.utils.batch_size.AutoBatchSize] = False)[source]
Bases:
object
Common methods for DeepPot, DeepWFC, DeepPolar, …
- Parameters
- model_file
Path
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
orAutomaticBatchSize
, default:False
If True, automatic batch size will be used. If int, it will be used as the initial batch size.
- model_file
- Attributes
model_type
Get type of model.
model_version
Get version of model.
Methods
make_natoms_vec
(atom_types)Make the natom vector used by deepmd-kit.
reverse_map
(vec, imap)Reverse mapping of a vector according to the index map
sort_input
(coord, atom_type[, sel_atoms])Sort atoms in the system according their types.
- make_natoms_vec(atom_types: numpy.ndarray) numpy.ndarray [source]
Make the natom vector used by deepmd-kit.
- Parameters
- atom_types
The type of atoms
- Returns
natoms
The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- static reverse_map(vec: numpy.ndarray, imap: List[int]) numpy.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.
- static sort_input(coord: numpy.ndarray, atom_type: numpy.ndarray, sel_atoms: Optional[List[int]] = None)[source]
Sort atoms in the system according their types.
- Parameters
- coord
The coordinates of atoms. Should be of shape [nframes, natoms, 3]
- atom_type
The type of atoms Should be of shape [natoms]
- sel_atom
The selected atoms by type
- Returns
coord_out
The coordinates after sorting
atom_type_out
The atom types after sorting
idx_map
The index mapping from the input to the output. For example coord_out = coord[:,idx_map,:]
sel_atom_type
Only output if sel_atoms is not None The sorted selected atom types
sel_idx_map
Only output if sel_atoms is not None The index mapping from the selected atoms to sorted selected atoms.
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:
deepmd.infer.deep_tensor.DeepTensor
Constructor.
- Parameters
- Attributes
model_type
Get type of model.
model_version
Get version of model.
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.
Unsupported in this model.
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)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: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[numpy.ndarray] = None, aparam: Optional[numpy.ndarray] = None, efield: Optional[numpy.ndarray] = None) numpy.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
- class deepmd.infer.deep_polar.DeepPolar(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False)[source]
Bases:
deepmd.infer.deep_tensor.DeepTensor
Constructor.
- Parameters
- model_file
Path
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
- model_file
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.
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.
Unsupported in this model.
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)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.
deepmd.infer.deep_pot module
- class deepmd.infer.deep_pot.DeepPot(model_file: Path, load_prefix: str = 'load', default_tf_graph: bool = False, auto_batch_size: Union[bool, int, deepmd.utils.batch_size.AutoBatchSize] = True)[source]
Bases:
deepmd.infer.deep_eval.DeepEval
Constructor.
- Parameters
- model_file
Path
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
orAutomaticBatchSize
, default:True
If True, automatic batch size will be used. If int, it will be used as the initial batch size.
- model_file
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.
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.
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_rcut
()Get the cut-off radius of this model.
Unsupported in this model.
Get the type map (element name of the atom types) of this model.
make_natoms_vec
(atom_types)Make the natom vector used by deepmd-kit.
reverse_map
(vec, imap)Reverse mapping of a vector according to the index map
sort_input
(coord, atom_type[, sel_atoms])Sort atoms in the system according their types.
- eval(coords: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[numpy.ndarray] = None, aparam: Optional[numpy.ndarray] = None, efield: Optional[numpy.ndarray] = None) Tuple[numpy.ndarray, ...] [source]
Evaluate the energy, force and virial by using this DP.
- Parameters
- coords
The coordinates of atoms. The array should be of size nframes x natoms x 3
- cells
The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9
- atom_types
The atom types The list should contain natoms ints
- atomic
Calculate the atomic energy and virial
- fparam
The frame parameter. The array can be of size : - nframes x dim_fparam. - dim_fparam. Then all frames are assumed to be provided with the same fparam.
- aparam
The atomic parameter The array can be of size : - nframes x natoms x dim_aparam. - natoms x dim_aparam. Then all frames are assumed to be provided with the same aparam. - dim_aparam. Then all frames and atoms are provided with the same aparam.
- efield
The external field on atoms. The array should be of size nframes x natoms x 3
- Returns
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: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], fparam: Optional[numpy.ndarray] = None, aparam: Optional[numpy.ndarray] = None, efield: Optional[numpy.ndarray] = None) numpy.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
- Returns
descriptor
Descriptors.
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:
deepmd.infer.deep_eval.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.
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.
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_rcut
()Get the cut-off 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.
make_natoms_vec
(atom_types)Make the natom vector used by deepmd-kit.
reverse_map
(vec, imap)Reverse mapping of a vector according to the index map
sort_input
(coord, atom_type[, sel_atoms])Sort atoms in the system according their types.
- eval(coords: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], atomic: bool = True, fparam: Optional[numpy.ndarray] = None, aparam: Optional[numpy.ndarray] = None, efield: Optional[numpy.ndarray] = None) numpy.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
- 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: numpy.ndarray, cells: numpy.ndarray, atom_types: List[int], atomic: bool = False, fparam: Optional[numpy.array] = None, aparam: Optional[numpy.array] = None, efield: Optional[numpy.array] = None) Tuple[numpy.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
- 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]
- 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:
deepmd.infer.deep_tensor.DeepTensor
Constructor.
- Parameters
- model_file
Path
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
- model_file
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.
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.
Unsupported in this model.
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)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.
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: numpy.ndarray, charge: numpy.ndarray, box: numpy.ndarray) Tuple[numpy.ndarray, numpy.ndarray, numpy.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, nopbc=True)[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
- nopbcbool
Whether to use pbc conditions
- coord
- Returns
- model_devi
numpy.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.
- model_devi
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: numpy.ndarray)[source]
- Parameters
- es
numpy.ndarray
size of `n_models x n_frames x n_atoms
- es
- deepmd.infer.model_devi.calc_model_devi_f(fs: numpy.ndarray)[source]
- Parameters
- fs
numpy.ndarray
size of n_models x n_frames x n_atoms x 3
- fs
- deepmd.infer.model_devi.calc_model_devi_v(vs: numpy.ndarray)[source]
- Parameters
- vs
numpy.ndarray
size of n_models x n_frames x 9
- vs
- 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
- 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.
- deepmd.infer.model_devi.write_model_devi_out(devi: numpy.ndarray, fname: str)[source]
- Parameters
- devi
numpy.ndarray
the first column is the steps index
- fname
str
the file name to dump
- devi