deepmd.jax.descriptor.se_t#
Classes#
DeepPot-SE constructed from all information (both angular and radial) of atomic |
Module Contents#
- class deepmd.jax.descriptor.se_t.DescrptSeT(rcut: float, rcut_smth: float, sel: list[int], neuron: list[int] = [24, 48, 96], resnet_dt: bool = False, set_davg_zero: bool = False, activation_function: str = 'tanh', env_protection: float = 0.0, exclude_types: list[tuple[int, int]] = [], precision: str = DEFAULT_PRECISION, trainable: bool = True, seed: int | list[int] | None = None, type_map: list[str] | None = None, ntypes: int | None = None)[source]#
Bases:
deepmd.dpmodel.descriptor.se_t.DescrptSeTDeepPot-SE constructed from all information (both angular and radial) of atomic configurations.
The embedding takes angles between two neighboring atoms as input.
The descriptor \(\mathcal{D}^i \in \mathbb{R}^{M}\) is given by
\[\mathcal{D}^i = \sum_{t_j, t_k} \frac{1}{N_{t_j} N_{t_k}} \sum_{j \in t_j, k \in t_k} \tilde{g}_{jk} \, \mathcal{N}_{t_j, t_k}(\tilde{g}_{jk}),\]where \(\tilde{g}_{jk} = \boldsymbol{rr}_j \cdot \boldsymbol{rr}_k\) is the dot product of the smoothed directional vectors from the environment matrix, \(N_{t_j}\) and \(N_{t_k}\) are the numbers of neighbors of types \(t_j\) and \(t_k\), and \(\mathcal{N}_{t_j, t_k}\) is the embedding network that depends only on the types of neighbor atoms \(j\) and \(k\).
The smoothed directional vector \(\boldsymbol{rr}_j\) is computed as:
\[\boldsymbol{rr}_j = s(r_{ji}) \frac{\boldsymbol{R}_j - \boldsymbol{R}_i}{r_{ji}},\]where \(s(r)\) is the switching function.
- Parameters:
- rcut
float The cut-off radius
- rcut_smth
float From where the environment matrix should be smoothed
- sel
list[int] sel[i] specifies the maxmum number of type i atoms in the cut-off radius
- neuron
list[int] Number of neurons in each hidden layers of the embedding net
- resnet_dtbool
Time-step dt in the resnet construction: y = x + dt * phi (Wx + b)
- set_davg_zerobool
Set the shift of embedding net input to zero.
- activation_function
str The activation function in the embedding net. Supported options are “silu”, “softplus”, “sigmoid”, “none”, “gelu_tf”, “relu6”, “tanh”, “linear”, “gelu”, “relu”, “silut”.
- env_protection
float Protection parameter to prevent division by zero errors during environment matrix calculations.
- exclude_types
list[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.
- precision
str The precision of the embedding net parameters. Supported options are “float32”, “bfloat16”, “default”, “float64”, “float16”.
- trainablebool
If the weights of embedding net are trainable.
- seed
int,Optional Random seed for initializing the network parameters.
- type_map: list[str], Optional
A list of strings. Give the name to each type of atoms.
- ntypes
int Number of element types. Not used in this descriptor, only to be compat with input.
- rcut