deepmd.dpmodel.descriptor.se_r

Module Contents

Classes

DescrptSeR

DeepPot-SE_R constructed from only the radial imformation of atomic configurations.

Attributes

__version__

deepmd.dpmodel.descriptor.se_r.__version__ = 'unknown'

class deepmd.dpmodel.descriptor.se_r.DescrptSeR(rcut: float, rcut_smth: float, sel: List[int], neuron: List[int] = [24, 48, 96], resnet_dt: bool = False, trainable: bool = True, type_one_side: bool = True, exclude_types: List[List[int]] = [], env_protection: float = 0.0, set_davg_zero: bool = False, activation_function: str = 'tanh', precision: str = DEFAULT_PRECISION, spin: Any | None = None, seed: int | None = None)

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

DeepPot-SE_R constructed from only the radial imformation of atomic configurations.

Parameters:

rcut

The cut-off radius \(r_c\)

rcut_smth

From where the environment matrix should be smoothed \(r_s\)

sellist[int]

sel[i] specifies the maxmum number of type i atoms in the cut-off radius

neuronlist[int]

Number of neurons in each hidden layers of the embedding net \(\mathcal{N}\)

resnet_dt

Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)

trainable

If the weights of embedding net are trainable.

type_one_side

Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets

exclude_typesList[List[int]]

The excluded pairs of types which have no interaction with each other. For example, [[0, 1]] means no interaction between type 0 and type 1.

set_davg_zero

Set the shift of embedding net input to zero.

activation_function

The activation function in the embedding net. Supported options are “relu”, “tanh”, “none”, “linear”, “softplus”, “sigmoid”, “relu6”, “gelu”, “gelu_tf”.

precision

The precision of the embedding net parameters. Supported options are “float32”, “default”, “float16”, “float64”.

multi_task

If the model has multi fitting nets to train.

spin

The deepspin object.

References

[1]

Linfeng Zhang, Jiequn Han, Han Wang, Wissam A. Saidi, Roberto Car, and E. Weinan. 2018. End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS’18). Curran Associates Inc., Red Hook, NY, USA, 4441-4451.

property dim_out

Returns the output dimension of this descriptor.

__setitem__(key, value)

__getitem__(key)

get_dim_out()

Returns the output dimension of this descriptor.

abstract get_dim_emb()

Returns the embedding (g2) dimension of this descriptor.

get_rcut()

Returns cutoff radius.

get_sel()

Returns cutoff radius.

mixed_types()

Returns if the descriptor requires a neighbor list that distinguish different atomic types or not.

abstract share_params(base_class, shared_level, resume=False)

Share the parameters of self to the base_class with shared_level during multitask training. If not start from checkpoint (resume is False), some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes.

get_ntypes() → int

Returns the number of element types.

abstract compute_input_stats(merged: List[dict], path: deepmd.utils.path.DPPath | None = None)

Update mean and stddev for descriptor elements.

cal_g(ss, ll)

call(coord_ext, atype_ext, nlist, mapping: numpy.ndarray | None = None)

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 from extended to lcoal region. not used 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. this descriptor returns None

h2

The rotationally equivariant pair-partical representation. this descriptor returns None

sw

The smooth switch function.

serialize() → dict

Serialize the descriptor to dict.

classmethod deserialize(data: dict) → DescrptSeR

Deserialize from dict.

classmethod update_sel(global_jdata: dict, local_jdata: dict)

Update the selection and perform neighbor statistics.

Parameters:

global_jdatadict

The global data, containing the training section

local_jdatadict

The local data refer to the current class