deepmd.tf.modifier

deepmd.tf.modifier#

Submodules#

Classes#

`BaseModifier`	Potential energy model.
`DipoleChargeModifier`

Package Contents#

class deepmd.tf.modifier.BaseModifier(*args: Any, **kwargs: Any)[source]#

Bases: deepmd.tf.infer.DeepPot, make_base_modifier()

Potential energy model.

Parameters:

model_filePath: The name of the frozen model file.
*argslist: Positional arguments.
auto_batch_sizebool or int or AutoBatchSize, default: True: If True, automatic batch size will be used. If int, it will be used as the initial batch size.
neighbor_listase.neighborlist.NewPrimitiveNeighborList, optional: The ASE neighbor list class to produce the neighbor list. If None, the neighbor list will be built natively in the model.
**kwargsdict: Keyword arguments.

Examples

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

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

static get_params_from_frozen_model(model: deepmd.tf.infer.DeepEval) → dict[source]#

Abstractmethod:

Extract the modifier parameters from a model.

This method should extract the necessary parameters from a model to create an instance of this modifier.

Parameters:

model: The model from which to extract parameters

Returns:

dict: The modifier parameters

class deepmd.tf.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.tf.infer.deep_dipole.DeepDipoleOld, deepmd.tf.modifier.base_modifier.BaseModifier

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}

modifier_prefix = 'dipole_charge'#

model_name#

model_charge_map#

sys_charge_map#

sel_type#

ewald_h = 1#

ewald_beta = 1#

er#

ext_dim = 3#

t_ndesc#

t_sela#

sel_r#

nnei_a#

nnei_r#

nnei#

ndescrpt_a#

ndescrpt_r#

force = None#

ntypes#

serialize() → dict[source]#

Serialize the modifier.

Returns:

dict: The serialized data

classmethod deserialize(data: dict) → deepmd.tf.modifier.base_modifier.BaseModifier[source]#

Deserialize the modifier.

Parameters:

datadict: The serialized data

Returns:

BaseModel: The deserialized modifier

build_fv_graph() → deepmd.tf.env.tf.Tensor[source]#: Build the computational graph for the force and virial inference.

_build_fv_graph_inner() → tuple[deepmd.tf.env.tf.Tensor, deepmd.tf.env.tf.Tensor, deepmd.tf.env.tf.Tensor][source]#

_enrich(dipole: deepmd.tf.env.tf.Tensor, dof: int = 3) → deepmd.tf.env.tf.Tensor[source]#

_slice_descrpt_deriv(deriv: deepmd.tf.env.tf.Tensor) → deepmd.tf.env.tf.Tensor[source]#

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

_eval_fv(coords: numpy.ndarray, cells: numpy.ndarray, atom_types: numpy.ndarray, ext_f: numpy.ndarray) → tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray][source]#

_extend_system(coord: numpy.ndarray, box: numpy.ndarray, atype: numpy.ndarray, charge: numpy.ndarray) → tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray][source]#

modify_data(data: dict, data_sys: deepmd.tf.utils.data.DeepmdData) → None[source]#

Modify data.

Parameters:

data: Internal data of DeepmdData. Be a dict, has the following keys - coord coordinates - box simulation box - type atom types - find_energy tells if data has energy - find_force tells if data has force - find_virial tells if data has virial - energy energy - force force - virial virial
data_sysDeepmdData: The data system.

static get_params_from_frozen_model(model: deepmd.tf.infer.DeepEval) → dict[source]#

Extract modifier parameters from a DeepEval model.

Parameters:

modelDeepEval: The DeepEval model instance containing the modifier tensors.

Returns:

dict: Dictionary containing modifier parameters: - model_name : str - model_charge_map : list[int] - sys_charge_map : list[int] - ewald_h : float - ewald_beta : float