deepmd.dpmodel.atomic_model.linear_atomic_model

deepmd.dpmodel.atomic_model.linear_atomic_model#

Classes#

`LinearEnergyAtomicModel`	Linear model make linear combinations of several existing models.
`DPZBLLinearEnergyAtomicModel`	Model linearly combine a list of AtomicModels.

Module Contents#

class deepmd.dpmodel.atomic_model.linear_atomic_model.LinearEnergyAtomicModel(models: list[deepmd.dpmodel.atomic_model.base_atomic_model.BaseAtomicModel], type_map: list[str], **kwargs)[source]#

Bases: deepmd.dpmodel.atomic_model.base_atomic_model.BaseAtomicModel

Linear model make linear combinations of several existing models.

Parameters:

modelslist[DPAtomicModel or PairTabAtomicModel]: A list of models to be combined. PairTabAtomicModel must be used together with a DPAtomicModel.
type_maplist[str]: Mapping atom type to the name (str) of the type. For example type_map[1] gives the name of the type 1.

models[source]#

type_map[source]#

mapping_list[source]#

mixed_types_list[source]#

mixed_types() → bool[source]#

If true, the model 1. assumes total number of atoms aligned across frames; 2. uses a neighbor list that does not distinguish different atomic types.

If false, the model 1. assumes total number of atoms of each atom type aligned across frames; 2. uses a neighbor list that distinguishes different atomic types.

has_message_passing() → bool[source]#: Returns whether the atomic model has message passing.

need_sorted_nlist_for_lower() → bool[source]#: Returns whether the atomic model needs sorted nlist when using forward_lower.

get_rcut() → float[source]#: Get the cut-off radius.

get_type_map() → list[str][source]#: Get the type map.

change_type_map(type_map: list[str], model_with_new_type_stat=None) → None[source]#: Change the type related params to new ones, according to type_map and the original one in the model. If there are new types in type_map, statistics will be updated accordingly to model_with_new_type_stat for these new types.

get_model_rcuts() → list[float][source]#: Get the cut-off radius for each individual models.

get_sel() → list[int][source]#: Returns the number of selected atoms for each type.

get_model_nsels() → list[int][source]#: Get the processed sels for each individual models. Not distinguishing types.

get_model_sels() → list[int | list[int]][source]#: Get the sels for each individual models.

_sort_rcuts_sels() → tuple[list[float], list[int]][source]#

enable_compression(min_nbor_dist: float, table_extrapolate: float = 5, table_stride_1: float = 0.01, table_stride_2: float = 0.1, check_frequency: int = -1) → None[source]#

Compress model.

Parameters:

min_nbor_dist: The nearest distance between atoms
table_extrapolate: The scale of model extrapolation
table_stride_1: The uniform stride of the first table
table_stride_2: The uniform stride of the second table
check_frequency: The overflow check frequency

forward_atomic(extended_coord, extended_atype, nlist, mapping: numpy.ndarray | None = None, fparam: numpy.ndarray | None = None, aparam: numpy.ndarray | None = None) → dict[str, numpy.ndarray][source]#

Return atomic prediction.

Parameters:

extended_coord: coordinates in extended region, (nframes, nall * 3)
extended_atype: atomic type in extended region, (nframes, nall)
nlist: neighbor list, (nframes, nloc, nsel).
mapping: mapps the extended indices to local indices.
fparam: frame parameter. (nframes, ndf)
aparam: atomic parameter. (nframes, nloc, nda)

Returns:

result_dict: the result dict, defined by the fitting net output def.

static remap_atype(ori_map: list[str], new_map: list[str]) → numpy.ndarray[source]#

This method is used to map the atype from the common type_map to the original type_map of indivial AtomicModels.

Parameters:

ori_maplist[str]: The original type map of an AtomicModel.
new_maplist[str]: The common type map of the DPZBLLinearEnergyAtomicModel, created by the get_type_map method, must be a subset of the ori_map.

Returns:

np.ndarray

fitting_output_def() → deepmd.dpmodel.output_def.FittingOutputDef[source]#: Get the output def of developer implemented atomic models.

serialize() → dict[source]#

classmethod deserialize(data: dict) → LinearEnergyAtomicModel[source]#

_compute_weight(extended_coord: numpy.ndarray, extended_atype: numpy.ndarray, nlists_: list[numpy.ndarray]) → list[numpy.ndarray][source]#: This should be a list of user defined weights that matches the number of models to be combined.

get_dim_fparam() → int[source]#: Get the number (dimension) of frame parameters of this atomic model.

get_dim_aparam() → int[source]#: Get the number (dimension) of atomic parameters of this atomic model.

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.

is_aparam_nall() → bool[source]#

Check whether the shape of atomic parameters is (nframes, nall, ndim).

If False, the shape is (nframes, nloc, ndim).

class deepmd.dpmodel.atomic_model.linear_atomic_model.DPZBLLinearEnergyAtomicModel(dp_model: deepmd.dpmodel.atomic_model.dp_atomic_model.DPAtomicModel, zbl_model: deepmd.dpmodel.atomic_model.pairtab_atomic_model.PairTabAtomicModel, sw_rmin: float, sw_rmax: float, type_map: list[str], smin_alpha: float | None = 0.1, **kwargs)[source]#

Bases: LinearEnergyAtomicModel

Model linearly combine a list of AtomicModels.

Parameters:

dp_model: The DPAtomicModel being combined.
zbl_model: The PairTable model being combined.
sw_rmin: The lower boundary of the interpolation between short-range tabulated interaction and DP.
sw_rmax: The upper boundary of the interpolation between short-range tabulated interaction and DP.
type_map: Mapping atom type to the name (str) of the type. For example type_map[1] gives the name of the type 1.
smin_alpha: The short-range tabulated interaction will be switched according to the distance of the nearest neighbor. This distance is calculated by softmin.

sw_rmin[source]#

sw_rmax[source]#

smin_alpha[source]#

serialize() → dict[source]#

classmethod deserialize(data) → DPZBLLinearEnergyAtomicModel[source]#

_compute_weight(extended_coord: numpy.ndarray, extended_atype: numpy.ndarray, nlists_: list[numpy.ndarray]) → list[numpy.ndarray][source]#

ZBL weight.

Returns:

list[np.ndarray]: the atomic ZBL weight for interpolation. (nframes, nloc, 1)