deepmd.dpmodel.model.spin_model

deepmd.dpmodel.model.spin_model#

Classes#

SpinModel

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

Module Contents#

class deepmd.dpmodel.model.spin_model.SpinModel(backbone_model: deepmd.dpmodel.atomic_model.dp_atomic_model.DPAtomicModel, spin: deepmd.utils.spin.Spin)[source]#

Bases: deepmd.dpmodel.common.NativeOP

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

This model extends a backbone DP model to handle magnetic spin degrees of freedom. Virtual atoms are created at positions offset from real atoms by their spin vectors:

\[\mathbf{r}_i^{\mathrm{virtual}} = \mathbf{r}_i^{\mathrm{real}} + s_i \cdot \boldsymbol{\sigma}_i,\]

where \(s_i\) is a scaling factor and \(\boldsymbol{\sigma}_i\) is the spin vector.

The model then computes interactions between real atoms, virtual atoms, and between real and virtual atoms, enabling the prediction of spin-dependent properties.

The output forces on virtual atoms are converted to magnetic torques:

\[\boldsymbol{\tau}_i = \mathbf{F}_i^{\mathrm{virtual}} \times \boldsymbol{\sigma}_i.\]

backbone_model[source]#

spin[source]#

ntypes_real[source]#

virtual_scale_mask[source]#

spin_mask[source]#

_to_xp(arr: Any, xp: Any, ref_arr: Any) → Any[source]#: Convert a numpy array to the same namespace as ref_arr.

process_spin_input(coord: deepmd.dpmodel.array_api.Array, atype: deepmd.dpmodel.array_api.Array, spin: deepmd.dpmodel.array_api.Array) → tuple[deepmd.dpmodel.array_api.Array, deepmd.dpmodel.array_api.Array, deepmd.dpmodel.array_api.Array][source]#

Generate virtual coordinates and types, concat into the input.

Returns:

coord_spinArray: Concatenated coordinates with shape (nframes, 2*nloc, 3).
atype_spinArray: Concatenated atom types with shape (nframes, 2*nloc).
coord_corrArray: Coordinate correction for virial with shape (nframes, 2*nloc, 3).

process_spin_input_lower(extended_coord: deepmd.dpmodel.array_api.Array, extended_atype: deepmd.dpmodel.array_api.Array, extended_spin: deepmd.dpmodel.array_api.Array, nlist: deepmd.dpmodel.array_api.Array, mapping: deepmd.dpmodel.array_api.Array | None = None) → tuple[deepmd.dpmodel.array_api.Array, deepmd.dpmodel.array_api.Array, deepmd.dpmodel.array_api.Array, deepmd.dpmodel.array_api.Array | None, deepmd.dpmodel.array_api.Array][source]#

Add extended_spin into extended_coord to generate virtual atoms, and extend nlist and mapping.

Returns:

extended_coord_updatedArray: Updated coordinates with virtual atoms, shape (nframes, 2*nall, 3).
extended_atype_updatedArray: Updated atom types with virtual atoms, shape (nframes, 2*nall).
nlist_updatedArray: Updated neighbor list including virtual atoms.
mapping_updatedArray or None: Updated mapping indices, or None if input mapping is None.
extended_coord_corrArray: Coordinate correction for virial with shape (nframes, 2*nall, 3).

Notes

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: deepmd.dpmodel.array_api.Array, out_tensor: deepmd.dpmodel.array_api.Array, add_mag: bool = True, virtual_scale: bool = True) → tuple[deepmd.dpmodel.array_api.Array, deepmd.dpmodel.array_api.Array][source]#: Split the output both real and virtual atoms, and scale the latter.

process_spin_output_lower(extended_atype: deepmd.dpmodel.array_api.Array, extended_out_tensor: deepmd.dpmodel.array_api.Array, nloc: int, add_mag: bool = True, virtual_scale: bool = True) → tuple[deepmd.dpmodel.array_api.Array, deepmd.dpmodel.array_api.Array][source]#: Split the extended output of both real and virtual atoms with switch, and scale the latter.

static extend_nlist(extended_atype: deepmd.dpmodel.array_api.Array, nlist: deepmd.dpmodel.array_api.Array) → deepmd.dpmodel.array_api.Array[source]#

static concat_switch_virtual(extended_tensor: deepmd.dpmodel.array_api.Array, extended_tensor_virtual: deepmd.dpmodel.array_api.Array, nloc: int) → deepmd.dpmodel.array_api.Array[source]#

static expand_aparam(aparam: deepmd.dpmodel.array_api.Array, nloc: int) → deepmd.dpmodel.array_api.Array[source]#: Expand the atom parameters for virtual atoms if necessary.

compute_or_load_stat(sampled_func: collections.abc.Callable[[], list[dict[str, Any]]], stat_file_path: Any | None = None, preset_observed_type: list[str] | None = None) → None[source]#

Compute or load the statistics parameters of the model.

Parameters:

sampled_func: The lazy sampled function to get data frames from different data systems.
stat_file_path: The dictionary of paths to the statistics files.
preset_observed_type: The preset observed types.

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

get_ntypes() → int[source]#: Returns the number of element types.

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

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).

model_output_type() → list[str][source]#: Get the output type for the model.

get_model_def_script() → str[source]#: Get the model definition script.

get_min_nbor_dist() → float | None[source]#: Get the minimum neighbor distance.

get_nnei() → int[source]#: Returns the total number of selected neighboring atoms in the cut-off radius.

get_nsel() → int[source]#: Returns the total number of selected neighboring atoms in the cut-off radius.

static has_spin() → bool[source]#: Returns whether it has spin input and output.

model_output_def() → deepmd.dpmodel.output_def.ModelOutputDef[source]#: Get the output def for the model.

_get_spin_sampled_func(sampled_func: collections.abc.Callable[[], list[dict]]) → collections.abc.Callable[[], list[dict]][source]#

Get a spin-aware sampled function that transforms spin data for the backbone model.

Parameters:

sampled_func: A callable that returns a list of data dicts containing ‘coord’, ‘atype’, ‘spin’, etc.

Returns:

Callable: A cached callable that returns spin-preprocessed data dicts.

change_out_bias(merged: collections.abc.Callable[[], list[dict]] | list[dict], bias_adjust_mode: str = 'change-by-statistic') → None[source]#

Change the output bias of atomic model according to the input data and the pretrained model.

Parameters:

mergedUnion[Callable[[], list[dict]], list[dict]]

list[dict]: A list of data samples from various data systems.
Each element, merged[i], is a data dictionary containing keys: np.ndarray originating from the i-th data system.
Callable[[], list[dict]]: A lazy function that returns data samples in the above format
only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once.

bias_adjust_modestr

The mode for changing output bias : [‘change-by-statistic’, ‘set-by-statistic’] ‘change-by-statistic’ : perform predictions on labels of target dataset,

and do least square on the errors to obtain the target shift as bias.

‘set-by-statistic’ : directly use the statistic output bias in the target dataset.

change_type_map(type_map: list[str], model_with_new_type_stat: Any = 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.

__getattr__(name: str) → Any[source]#: Get attribute from the wrapped model.

get_dp_atomic_model() → DPAtomicModel | None[source]#: Get the underlying DPAtomicModel by delegating to the backbone model.

serialize() → dict[source]#

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

call_common(coord: deepmd.dpmodel.array_api.Array, atype: deepmd.dpmodel.array_api.Array, spin: deepmd.dpmodel.array_api.Array, box: deepmd.dpmodel.array_api.Array | None = None, fparam: deepmd.dpmodel.array_api.Array | None = None, aparam: deepmd.dpmodel.array_api.Array | None = None, do_atomic_virial: bool = False, charge_spin: deepmd.dpmodel.array_api.Array | None = None) → dict[str, deepmd.dpmodel.array_api.Array][source]#

Return model prediction with raw internal keys.

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(coord: deepmd.dpmodel.array_api.Array, atype: deepmd.dpmodel.array_api.Array, spin: deepmd.dpmodel.array_api.Array, box: deepmd.dpmodel.array_api.Array | None = None, fparam: deepmd.dpmodel.array_api.Array | None = None, aparam: deepmd.dpmodel.array_api.Array | None = None, do_atomic_virial: bool = False, charge_spin: deepmd.dpmodel.array_api.Array | None = None) → dict[str, deepmd.dpmodel.array_api.Array][source]#

Return model prediction with translated user-facing keys.

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 with translated keys, e.g. atom_energy, energy, force, force_mag.

Return model prediction with raw internal keys. 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.

Return model prediction with translated user-facing keys. Lower interface.

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 with translated keys, e.g. atom_energy, energy, extended_force, extended_force_mag.

translated_output_def() → dict[str, Any][source]#

Get the translated output definition.

Maps internal output names to user-facing names, e.g. energy -> atom_energy, energy_redu -> energy, energy_derv_r -> force, energy_derv_r_mag -> force_mag.