deepmd.dpmodel.descriptor.hybrid

deepmd.dpmodel.descriptor.hybrid#

Classes#

DescrptHybrid

Concatenate a list of descriptors to form a new descriptor.

Module Contents#

class deepmd.dpmodel.descriptor.hybrid.DescrptHybrid(list: DescrptHybrid.__init__.list[deepmd.dpmodel.descriptor.base_descriptor.BaseDescriptor | dict[str, Any]], type_map: DescrptHybrid.__init__.list[str] | None = None, ntypes: int | None = None)[source]#

Bases: deepmd.dpmodel.descriptor.base_descriptor.BaseDescriptor, deepmd.dpmodel.common.NativeOP

Concatenate a list of descriptors to form a new descriptor.

The hybrid descriptor combines multiple descriptors by concatenation:

\[\mathcal{D}^i = [\mathcal{D}^i_1, \mathcal{D}^i_2, ..., \mathcal{D}^i_n],\]

where \(\mathcal{D}^i_k\) is the descriptor computed by the \(k\)-th sub-descriptor for atom \(i\).

The output dimension is the sum of all sub-descriptor dimensions:

\[\dim(\mathcal{D}^i) = \sum_{k=1}^{n} \dim(\mathcal{D}^i_k).\]

Parameters:

listlistlist[Union[BaseDescriptor, dict[str, Any]]]: Build a descriptor from the concatenation of the list of descriptors. The descriptor can be either an object or a dictionary.

descrpt_list = [][source]#

numb_descrpt = 0[source]#

nlist_cut_idx = [][source]#

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

get_rcut_smth() → float[source]#: Returns the radius where the neighbor information starts to smoothly decay to 0.

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

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

get_type_map() → list[str][source]#: Get the name to each type of atoms.

get_dim_out() → int[source]#: Returns the output dimension.

get_dim_emb() → int[source]#: Returns the output dimension.

mixed_types() → bool[source]#: Returns if the descriptor requires a neighbor list that distinguish different atomic types or not.

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

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

get_env_protection() → float[source]#: Returns the protection of building environment matrix. All descriptors should be the same.

abstractmethod share_params(base_class: Any, shared_level: Any, resume: bool = False) → NoReturn[source]#: Share the parameters of self to the base_class with shared_level during multitask training. If not start from checkpoint (resume is False), some separated parameters (e.g. mean and stddev) will be re-calculated across different classes.

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.

compute_input_stats(merged: list[dict], path: deepmd.utils.path.DPPath | None = None) → None[source]#: Update mean and stddev for descriptor elements.

set_stat_mean_and_stddev(mean: list[numpy.ndarray | list[deepmd.dpmodel.array_api.Array]], stddev: list[numpy.ndarray | list[deepmd.dpmodel.array_api.Array]]) → None[source]#: Update mean and stddev for descriptor.

get_stat_mean_and_stddev() → tuple[list[deepmd.dpmodel.array_api.Array | list[deepmd.dpmodel.array_api.Array]], list[deepmd.dpmodel.array_api.Array | list[deepmd.dpmodel.array_api.Array]]][source]#: Get mean and stddev for descriptor.

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]#

Receive the statistics (distance, max_nbor_size and env_mat_range) of the training data.

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

call(coord_ext: deepmd.dpmodel.array_api.Array, atype_ext: deepmd.dpmodel.array_api.Array, nlist: deepmd.dpmodel.array_api.Array, mapping: deepmd.dpmodel.array_api.Array | None = None, fparam: deepmd.dpmodel.array_api.Array | None = None) → tuple[deepmd.dpmodel.array_api.Array, deepmd.dpmodel.array_api.Array | None, deepmd.dpmodel.array_api.Array | None, deepmd.dpmodel.array_api.Array | None, deepmd.dpmodel.array_api.Array | None][source]#

Compute the descriptor.

Parameters:

coord_ext: The extended coordinates of atoms. shape: nf x (nallx3)
atype_ext: The extended aotm types. shape: nf x nall
nlist: The neighbor list. shape: nf x nloc x nnei
mapping: The index mapping, not required by this descriptor.

Returns:

descriptor: The descriptor. shape: nf x nloc x (ng x axis_neuron)
gr: The rotationally equivariant and permutationally invariant single particle representation. shape: nf x nloc x ng x 3.
g2: The rotationally invariant pair-partical representation.
h2: The rotationally equivariant pair-partical representation.
sw: The smooth switch function.

classmethod update_sel(train_data: deepmd.utils.data_system.DeepmdDataSystem, type_map: list[str] | None, local_jdata: dict) → tuple[deepmd.dpmodel.array_api.Array, deepmd.dpmodel.array_api.Array][source]#

Update the selection and perform neighbor statistics.

Parameters:

train_dataDeepmdDataSystem: data used to do neighbor statistics
type_maplist[str], optional: The name of each type of atoms
local_jdatadict: The local data refer to the current class

Returns:

dict: The updated local data
float: The minimum distance between two atoms

serialize() → dict[source]#: Serialize the obj to dict.

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

Deserialize the model.

Parameters:

datadict: The serialized data

Returns:

BD: The deserialized descriptor