deepmd.dpmodel.atomic_model

The atomic model provides the prediction of some property on each atom. All the atomic models are not supposed to be directly accessed by users, but it provides a convenient interface for the implementation of models.

Taking the energy models for example, the developeres only needs to implement the atomic energy prediction via an atomic model, and the model can be automatically made by the deepmd.dpmodel.make_model method. The DPModel is made by ` DPModel = make_model(DPAtomicModel) `

Submodules

• deepmd.dpmodel.atomic_model.base_atomic_model
• deepmd.dpmodel.atomic_model.dp_atomic_model
• deepmd.dpmodel.atomic_model.linear_atomic_model
• deepmd.dpmodel.atomic_model.make_base_atomic_model
• deepmd.dpmodel.atomic_model.pairtab_atomic_model

Package Contents

Classes

BaseAtomicModel	Base Atomic Model provides the interfaces of an atomic model.
DPAtomicModel	Model give atomic prediction of some physical property.
DPZBLLinearEnergyAtomicModel	Model linearly combine a list of AtomicModels.
LinearEnergyAtomicModel	Linear model make linear combinations of several existing models.
PairTabAtomicModel	Pairwise tabulation energy model.

Functions

make_base_atomic_model(t_tensor[, fwd_method_name])

Make the base class for the atomic model.

class deepmd.dpmodel.atomic_model.BaseAtomicModel(type_map: List[str], atom_exclude_types: List[int] = [], pair_exclude_types: List[Tuple[int, int]] = [], rcond: float | None = None, preset_out_bias: Dict[str, numpy.ndarray] | None = None)

Bases: BaseAtomicModel_

Base Atomic Model provides the interfaces of an atomic model.

init_out_stat()

Initialize the output bias.

__setitem__(key, value)

__getitem__(key)

get_type_map() → List[str]

Get the type map.

reinit_atom_exclude(exclude_types: List[int] = [])

reinit_pair_exclude(exclude_types: List[Tuple[int, int]] = [])

atomic_output_def() → deepmd.dpmodel.output_def.FittingOutputDef

Get the output def of the atomic model.

By default it is the same as FittingOutputDef, but it allows model level wrapper of the output defined by the developer.

forward_common_atomic(extended_coord: numpy.ndarray, extended_atype: numpy.ndarray, nlist: numpy.ndarray, mapping: numpy.ndarray | None = None, fparam: numpy.ndarray | None = None, aparam: numpy.ndarray | None = None) → Dict[str, numpy.ndarray]

Common interface for atomic inference.

This method accept extended coordinates, extended atom typs, neighbor list, and predict the atomic contribution of the fit property.

Parameters:

extended_coord

extended coodinates, shape: nf x (nall x 3)

extended_atype

extended atom typs, shape: nf x nall for a type < 0 indicating the atomic is virtual.

nlist

neighbor list, shape: nf x nloc x nsel

mapping

extended to local index mapping, shape: nf x nall

fparam

frame parameters, shape: nf x dim_fparam

aparam

atomic parameter, shape: nf x nloc x dim_aparam

Returns:

ret_dict

dict of output atomic properties. should implement the definition of fitting_output_def. ret_dict[“mask”] of shape nf x nloc will be provided. ret_dict[“mask”][ff,ii] == 1 indicating the ii-th atom of the ff-th frame is real. ret_dict[“mask”][ff,ii] == 0 indicating the ii-th atom of the ff-th frame is virtual.

serialize() → dict

classmethod deserialize(data: dict) → BaseAtomicModel

apply_out_stat(ret: Dict[str, numpy.ndarray], atype: numpy.ndarray)

Apply the stat to each atomic output. The developer may override the method to define how the bias is applied to the atomic output of the model.

Parameters:

ret

The returned dict by the forward_atomic method

atype

The atom types. nf x nloc

_varsize(shape: List[int]) → int

_get_bias_index(kk: str) → int

_fetch_out_stat(keys: List[str]) → Tuple[Dict[str, numpy.ndarray], Dict[str, numpy.ndarray]]

class deepmd.dpmodel.atomic_model.DPAtomicModel(descriptor, fitting, type_map: List[str], **kwargs)

Bases: deepmd.dpmodel.atomic_model.base_atomic_model.BaseAtomicModel

Model give atomic prediction of some physical property.

Parameters:

descriptor

Descriptor

fitting_net

Fitting net

type_map

Mapping atom type to the name (str) of the type. For example type_map[1] gives the name of the type 1.

fitting_output_def() → deepmd.dpmodel.output_def.FittingOutputDef

Get the output def of the fitting net.

get_rcut() → float

Get the cut-off radius.

get_sel() → List[int]

Get the neighbor selection.

mixed_types() → bool

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.

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

Models’ atomic predictions.

Parameters:

extended_coord

coodinates in extended region

extended_atype

atomic type in extended region

nlist

neighbor list. nf x nloc x nsel

mapping

mapps the extended indices to local indices. nf x nall

fparam

frame parameter. nf x ndf

aparam

atomic parameter. nf x nloc x nda

Returns:

result_dict

the result dict, defined by the FittingOutputDef.

serialize() → dict

classmethod deserialize(data) → DPAtomicModel

get_dim_fparam() → int

Get the number (dimension) of frame parameters of this atomic model.

get_dim_aparam() → int

Get the number (dimension) of atomic parameters of this atomic model.

get_sel_type() → List[int]

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

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

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

class deepmd.dpmodel.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)

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 swithed according to the distance of the nearest neighbor. This distance is calculated by softmin.

serialize() → dict

classmethod deserialize(data) → DPZBLLinearEnergyAtomicModel

_compute_weight(extended_coord: numpy.ndarray, extended_atype: numpy.ndarray, nlists_: List[numpy.ndarray]) → List[numpy.ndarray]

ZBL weight.

Returns:

List[np.ndarray]

the atomic ZBL weight for interpolation. (nframes, nloc, 1)

class deepmd.dpmodel.atomic_model.LinearEnergyAtomicModel(models: List[deepmd.dpmodel.atomic_model.base_atomic_model.BaseAtomicModel], type_map: List[str], **kwargs)

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.

mixed_types() → bool

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.

get_rcut() → float

Get the cut-off radius.

get_type_map() → List[str]

Get the type map.

get_model_rcuts() → List[float]

Get the cut-off radius for each individual models.

get_sel() → List[int]

Returns the number of selected atoms for each type.

get_model_nsels() → List[int]

Get the processed sels for each individual models. Not distinguishing types.

get_model_sels() → List[int | List[int]]

Get the sels for each individual models.

_sort_rcuts_sels() → Tuple[List[float], List[int]]

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]

Return atomic prediction.

Parameters:

extended_coord

coodinates 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

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

Get the output def of developer implemented atomic models.

serialize() → dict

classmethod deserialize(data: dict) → LinearEnergyAtomicModel

_compute_weight(extended_coord: numpy.ndarray, extended_atype: numpy.ndarray, nlists_: List[numpy.ndarray]) → List[numpy.ndarray]

This should be a list of user defined weights that matches the number of models to be combined.

get_dim_fparam() → int

Get the number (dimension) of frame parameters of this atomic model.

get_dim_aparam() → int

Get the number (dimension) of atomic parameters of this atomic model.

get_sel_type() → List[int]

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

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

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

deepmd.dpmodel.atomic_model.make_base_atomic_model(t_tensor, fwd_method_name: str = 'forward_atomic')

Make the base class for the atomic model.

Parameters:

t_tensor

The type of the tensor. used in the type hint.

fwd_method_name

Name of the forward method. For dpmodels, it should be “call”. For torch models, it should be “forward”.

class deepmd.dpmodel.atomic_model.PairTabAtomicModel(tab_file: str, rcut: float, sel: int | List[int], type_map: List[str], rcond: float | None = None, atom_ener: List[float] | None = None, **kwargs)

Bases: deepmd.dpmodel.atomic_model.base_atomic_model.BaseAtomicModel

Pairwise tabulation energy model.

This model can be used to tabulate the pairwise energy between atoms for either short-range or long-range interactions, such as D3, LJ, ZBL, etc. It should not be used alone, but rather as one submodel of a linear (sum) model, such as DP+D3.

Do not put the model on the first model of a linear model, since the linear model fetches the type map from the first model.

At this moment, the model does not smooth the energy at the cutoff radius, so one needs to make sure the energy has been smoothed to zero.

Parameters:

tab_filestr

The path to the tabulation file.

rcutfloat

The cutoff radius.

selint or list[int]

The maxmum number of atoms in the cut-off radius.

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.

fitting_output_def() → deepmd.dpmodel.output_def.FittingOutputDef

Get the output def of developer implemented atomic models.

get_rcut() → float

Get the cut-off radius.

get_type_map() → List[str]

Get the type map.

get_sel() → List[int]

Returns the number of selected atoms for each type.

get_nsel() → int

Returns the total number of selected neighboring atoms in the cut-off radius.

mixed_types() → bool

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.

serialize() → dict

classmethod deserialize(data) → PairTabAtomicModel

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]

_pair_tabulated_inter(nlist: numpy.ndarray, i_type: numpy.ndarray, j_type: numpy.ndarray, rr: numpy.ndarray) → numpy.ndarray

Pairwise tabulated energy.

Parameters:

nlistnp.ndarray

The unmasked neighbour list. (nframes, nloc)

i_typenp.ndarray

The integer representation of atom type for all local atoms for all frames. (nframes, nloc)

j_typenp.ndarray

The integer representation of atom type for all neighbour atoms of all local atoms for all frames. (nframes, nloc, nnei)

rrnp.ndarray

The salar distance vector between two atoms. (nframes, nloc, nnei)

Returns:

np.ndarray

The masked atomic energy for all local atoms for all frames. (nframes, nloc, nnei)

Raises:

Exception

If the distance is beyond the table.

Notes

This function is used to calculate the pairwise energy between two atoms. It uses a table containing cubic spline coefficients calculated in PairTab.

static _get_pairwise_dist(coords: numpy.ndarray, nlist: numpy.ndarray) → numpy.ndarray

Get pairwise distance dr.

Parameters:

coordsnp.ndarray

The coordinate of the atoms, shape of (nframes, nall, 3).

nlist

The masked nlist, shape of (nframes, nloc, nnei).

Returns:

np.ndarray

The pairwise distance between the atoms (nframes, nloc, nnei).

static _extract_spline_coefficient(i_type: numpy.ndarray, j_type: numpy.ndarray, idx: numpy.ndarray, tab_data: numpy.ndarray, nspline: int) → numpy.ndarray

Extract the spline coefficient from the table.

Parameters:

i_typenp.ndarray

The integer representation of atom type for all local atoms for all frames. (nframes, nloc)

j_typenp.ndarray

The integer representation of atom type for all neighbour atoms of all local atoms for all frames. (nframes, nloc, nnei)

idxnp.ndarray

The index of the spline coefficient. (nframes, nloc, nnei)

tab_datanp.ndarray

The table storing all the spline coefficient. (ntype, ntype, nspline, 4)

nsplineint

The number of splines in the table.

Returns:

np.ndarray

The spline coefficient. (nframes, nloc, nnei, 4), shape may be squeezed.

static _calculate_ener(coef: numpy.ndarray, uu: numpy.ndarray) → numpy.ndarray

Calculate energy using spline coeeficients.

Parameters:

coefnp.ndarray

The spline coefficients. (nframes, nloc, nnei, 4)

uunp.ndarray

The atom displancemnt used in interpolation and extrapolation (nframes, nloc, nnei)

Returns:

np.ndarray

The atomic energy for all local atoms for all frames. (nframes, nloc, nnei)

get_dim_fparam() → int

Get the number (dimension) of frame parameters of this atomic model.

get_dim_aparam() → int

Get the number (dimension) of atomic parameters of this atomic model.

get_sel_type() → List[int]

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

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

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