deepmd.tf.model.pairtab

deepmd.tf.model.pairtab#

Classes#

PairTabModel

Pairwise tabulation energy model.

Module Contents#

class deepmd.tf.model.pairtab.PairTabModel(tab_file: str, rcut: float, sel: int | list[int], **kwargs: Any)[source]#

Bases: deepmd.tf.model.model.Model

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

model_type = 'ener'[source]#

tab_file[source]#

tab[source]#

ntypes[source]#

rcut[source]#

build(coord_: deepmd.tf.env.tf.Tensor, atype_: deepmd.tf.env.tf.Tensor, natoms: deepmd.tf.env.tf.Tensor, box: deepmd.tf.env.tf.Tensor, mesh: deepmd.tf.env.tf.Tensor, input_dict: dict, frz_model: str | None = None, ckpt_meta: str | None = None, suffix: str = '', reuse: bool | enum.Enum | None = None) → dict[source]#

Build the model.

Parameters:

coord_tf.Tensor: The coordinates of atoms
atype_tf.Tensor: The atom types of atoms
natomstf.Tensor: The number of atoms
boxtf.Tensor: The box vectors
meshtf.Tensor: The mesh vectors
input_dictdict: The input dict
frz_modelstr, optional: The path to the frozen model
ckpt_metastr, optional: The path prefix of the checkpoint and meta files
suffixstr, optional: The suffix of the scope
reusebool or tf.AUTO_REUSE, optional: Whether to reuse the variables

Returns:

dict: The output dict

init_variables(graph: deepmd.tf.env.tf.Graph, graph_def: deepmd.tf.env.tf.GraphDef, model_type: str = 'original_model', suffix: str = '') → None[source]#

Init the embedding net variables with the given frozen model.

Parameters:

graphtf.Graph: The input frozen model graph
graph_deftf.GraphDef: The input frozen model graph_def
model_typestr: the type of the model
suffixstr: suffix to name scope

get_fitting() → deepmd.tf.fit.fitting.Fitting | dict[source]#: Get the fitting(s).

get_loss(loss: dict, lr: deepmd.tf.utils.learning_rate.LearningRateExp) → deepmd.tf.loss.loss.Loss | dict | None[source]#: Get the loss function(s).

get_rcut() → float[source]#: Get cutoff radius of the model.

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

data_stat(data: dict) → None[source]#: Data staticis.

enable_compression(suffix: str = '') → None[source]#

Enable compression.

Parameters:

suffixstr: suffix to name scope

classmethod update_sel(train_data: deepmd.utils.data_system.DeepmdDataSystem, type_map: list[str] | None, local_jdata: dict) → tuple[dict, float | None][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

Notes

Do not modify the input data without copying it.

property input_requirement: list[deepmd.utils.data.DataRequirementItem][source]#: Return data requirements needed for the model input.