deepmd.dpmodel.model

The model that takes the coordinates, cell and atom types as input and predicts some property. The models are automatically generated from atomic models by the deepmd.dpmodel.make_model method.

The make_model method does the reduction, auto-differentiation (dummy for dpmodels) and communication of the atomic properties according to output variable definition deepmd.dpmodel.OutputVariableDef.

All models should be inherited from deepmd.dpmodel.model.base_model.BaseModel. Models generated by make_model have already done it.

Submodules

• deepmd.dpmodel.model.base_model
• deepmd.dpmodel.model.dp_model
• deepmd.dpmodel.model.ener_model
• deepmd.dpmodel.model.make_model
• deepmd.dpmodel.model.model
• deepmd.dpmodel.model.spin_model
• deepmd.dpmodel.model.transform_output

Package Contents

Classes

DPModelCommon
SpinModel	A spin model wrapper, with spin input preprocess and output split.

Functions

make_model(T_AtomicModel)

Make a model as a derived class of an atomic model.

class deepmd.dpmodel.model.DPModelCommon

classmethod update_sel(global_jdata: dict, local_jdata: dict)

Update the selection and perform neighbor statistics.

Parameters:

global_jdatadict

The global data, containing the training section

local_jdatadict

The local data refer to the current class

deepmd.dpmodel.model.make_model(T_AtomicModel: Type[deepmd.dpmodel.atomic_model.base_atomic_model.BaseAtomicModel])

Make a model as a derived class of an atomic model.

The model provide two interfaces.

1. the call_lower, that takes extended coordinates, atyps and neighbor list, and outputs the atomic and property and derivatives (if required) on the extended region.

2. the call, that takes coordinates, atypes and cell and predicts the atomic and reduced property, and derivatives (if required) on the local region.

Parameters:

T_AtomicModel

The atomic model.

Returns:

CM

The model.

class deepmd.dpmodel.model.SpinModel(backbone_model, spin: deepmd.utils.spin.Spin)

A spin model wrapper, with spin input preprocess and output split.

process_spin_input(coord, atype, spin)

Generate virtual coordinates and types, concat into the input.

process_spin_input_lower(extended_coord: numpy.ndarray, extended_atype: numpy.ndarray, extended_spin: numpy.ndarray, nlist: numpy.ndarray, mapping: numpy.ndarray | None = None)

Add extended_spin into extended_coord to generate virtual atoms, and extend nlist and mapping. Note that the final extended_coord_updated with shape [nframes, nall + nall, 3] has the following order: - [:, :nloc]: original nloc real atoms. - [:, nloc: nloc + nloc]: virtual atoms corresponding to nloc real atoms. - [:, nloc + nloc: nloc + nall]: ghost real atoms. - [:, nloc + nall: nall + nall]: virtual atoms corresponding to ghost real atoms.

process_spin_output(atype, out_tensor, add_mag: bool = True, virtual_scale: bool = True)

Split the output both real and virtual atoms, and scale the latter.

process_spin_output_lower(extended_atype, extended_out_tensor, nloc: int, add_mag: bool = True, virtual_scale: bool = True)

Split the extended output of both real and virtual atoms with switch, and scale the latter.

static extend_nlist(extended_atype, nlist)

static concat_switch_virtual(extended_tensor, extended_tensor_virtual, nloc: int)

get_type_map() → List[str]

Get the type map.

get_rcut()

Get the cut-off radius.

get_dim_fparam()

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

get_dim_aparam()

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).

model_output_type() → List[str]

Get the output type for the model.

get_model_def_script() → str

Get the model definition script.

get_nnei() → int

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

get_nsel() → int

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

static has_spin() → bool

Returns whether it has spin input and output.

__getattr__(name)

Get attribute from the wrapped model.

serialize() → dict

classmethod deserialize(data) → SpinModel

call(coord, atype, spin, box: numpy.ndarray | None = None, fparam: numpy.ndarray | None = None, aparam: numpy.ndarray | None = None, do_atomic_virial: bool = False) → Dict[str, numpy.ndarray]

Return model prediction.

Parameters:

coord

The coordinates of the atoms. shape: nf x (nloc x 3)

atype

The type of atoms. shape: nf x nloc

spin

The spins of the atoms. shape: nf x (nloc x 3)

box

The simulation box. shape: nf x 9

fparam

frame parameter. nf x ndf

aparam

atomic parameter. nf x nloc x nda

do_atomic_virial

If calculate the atomic virial.

Returns:

ret_dict

The result dict of type Dict[str,np.ndarray]. The keys are defined by the ModelOutputDef.

call_lower(extended_coord: numpy.ndarray, extended_atype: numpy.ndarray, extended_spin: numpy.ndarray, nlist: numpy.ndarray, mapping: numpy.ndarray | None = None, fparam: numpy.ndarray | None = None, aparam: numpy.ndarray | None = None, do_atomic_virial: bool = False)

Return model prediction. Lower interface that takes extended atomic coordinates, types and spins, nlist, and mapping as input, and returns the predictions on the extended region. The predictions are not reduced.

Parameters:

extended_coord

coordinates in extended region. nf x (nall x 3).

extended_atype

atomic type in extended region. nf x nall.

extended_spin

spins in extended region. nf x (nall x 3).

nlist

neighbor list. nf x nloc x nsel.

mapping

maps the extended indices to local indices. nf x nall.

fparam

frame parameter. nf x ndf

aparam

atomic parameter. nf x nloc x nda

do_atomic_virial

whether calculate atomic virial

Returns:

result_dict

the result dict, defined by the FittingOutputDef.