deepmd.pt.model.atomic_model.dp_atomic_model#
Attributes#
Classes#
Model give atomic prediction of some physical property. |
Module Contents#
- class deepmd.pt.model.atomic_model.dp_atomic_model.DPAtomicModel(descriptor, fitting, type_map: list[str], **kwargs)[source]#
Bases:
deepmd.pt.model.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.
- set_eval_descriptor_hook(enable: bool) None [source]#
Set the hook for evaluating descriptor and clear the cache for descriptor list.
- fitting_output_def() deepmd.dpmodel.FittingOutputDef [source]#
Get the output def of the fitting net.
- 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.
- 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.
- need_sorted_nlist_for_lower() bool [source]#
Returns whether the atomic model needs sorted nlist when using forward_lower.
- classmethod deserialize(data) DPAtomicModel [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]#
Call descriptor enable_compression().
- 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: torch.Tensor | None = None, fparam: torch.Tensor | None = None, aparam: torch.Tensor | None = None, comm_dict: dict[str, torch.Tensor] | None = None) dict[str, torch.Tensor] [source]#
Return atomic prediction.
- Parameters:
- extended_coord
coordinates 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
- fparam
frame parameter. nf x ndf
- aparam
atomic parameter. nf x nloc x nda
- Returns:
result_dict
the result dict, defined by the FittingOutputDef.
- compute_or_load_stat(sampled_func, stat_file_path: deepmd.utils.path.DPPath | None = None) None [source]#
Compute or load the statistics parameters of the model, such as mean and standard deviation of descriptors or the energy bias of the fitting net. When sampled is provided, all the statistics parameters will be calculated (or re-calculated for update), and saved in the stat_file_path`(s). When `sampled is not provided, it will check the existence of `stat_file_path`(s) and load the calculated statistics parameters.
- 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.