deepmd.tf.model.pairwise_dprc

deepmd.tf.model.pairwise_dprc#

Classes#

PairwiseDPRc

Pairwise Deep Potential - Range Correction.

Functions#

gather_placeholder(→ deepmd.tf.env.tf.Tensor)

Call tf.gather but allow indices to contain placeholders (-1).

Module Contents#

class deepmd.tf.model.pairwise_dprc.PairwiseDPRc(qm_model: dict, qmmm_model: dict, type_embedding: dict | deepmd.tf.utils.type_embed.TypeEmbedNet, type_map: list[str], data_stat_nbatch: int = 10, data_stat_nsample: int = 10, data_stat_protect: float = 0.01, use_srtab: str | None = None, smin_alpha: float | None = None, sw_rmin: float | None = None, sw_rmax: float | None = None, spin: deepmd.tf.utils.spin.Spin | None = None, compress: dict | None = None, **kwargs)[source]#

Bases: deepmd.tf.model.model.Model

Pairwise Deep Potential - Range Correction.

model_type = 'ener'[source]#

merge_frames = True[source]#

ntypes[source]#

qm_model[source]#

qmmm_model[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=None, ckpt_meta: str | None = None, suffix: str = '', reuse: bool | None = None)[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

get_fitting() → str | dict[source]#: Get the fitting(s).

get_loss(loss: dict, lr) → deepmd.tf.loss.loss.Loss | dict[source]#: Get the loss function(s).

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

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

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

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

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

Enable compression.

Parameters:

suffixstr: suffix to name scope

get_feed_dict(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, **kwargs) → dict[str, deepmd.tf.env.tf.Tensor][source]#

Generate the feed_dict for current descriptor.

Parameters:

coord_tf.Tensor: The coordinate of atoms
atype_tf.Tensor: The type of atoms
natomstf.Tensor: The number of atoms. This tensor has the length of Ntypes + 2 natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
boxtf.Tensor: The box. Can be generated by deepmd.tf.model.make_stat_input
meshtf.Tensor: For historical reasons, only the length of the Tensor matters. if size of mesh == 6, pbc is assumed. if size of mesh == 0, no-pbc is assumed.
aparamtf.Tensor: The parameters of the descriptor
**kwargsdict: The keyword arguments

Returns:

feed_dictdict[str, tf.Tensor]: The output feed_dict of current descriptor

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

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

deepmd.tf.model.pairwise_dprc.gather_placeholder(params: deepmd.tf.env.tf.Tensor, indices: deepmd.tf.env.tf.Tensor, placeholder: float = 0.0, **kwargs) → deepmd.tf.env.tf.Tensor[source]#: Call tf.gather but allow indices to contain placeholders (-1).