deepmd.dpmodel.descriptor.se_e2_a
Module Contents
Classes
DeepPot-SE constructed from all information (both angular and radial) of |
Attributes
- class deepmd.dpmodel.descriptor.se_e2_a.DescrptSeA(rcut: float, rcut_smth: float, sel: List[int], neuron: List[int] = [24, 48, 96], axis_neuron: int = 8, 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)[source]
Bases:
deepmd.dpmodel.NativeOP,deepmd.dpmodel.descriptor.base_descriptor.BaseDescriptorDeepPot-SE constructed from all information (both angular and radial) of atomic configurations. The embedding takes the distance between atoms as input.
The descriptor \(\mathcal{D}^i \in \mathcal{R}^{M_1 \times M_2}\) is given by [1]
\[\mathcal{D}^i = (\mathcal{G}^i)^T \mathcal{R}^i (\mathcal{R}^i)^T \mathcal{G}^i_<\]where \(\mathcal{R}^i \in \mathbb{R}^{N \times 4}\) is the coordinate matrix, and each row of \(\mathcal{R}^i\) can be constructed as follows
\[(\mathcal{R}^i)_j = [ \begin{array}{c} s(r_{ji}) & \frac{s(r_{ji})x_{ji}}{r_{ji}} & \frac{s(r_{ji})y_{ji}}{r_{ji}} & \frac{s(r_{ji})z_{ji}}{r_{ji}} \end{array} ]\]where \(\mathbf{R}_{ji}=\mathbf{R}_j-\mathbf{R}_i = (x_{ji}, y_{ji}, z_{ji})\) is the relative coordinate and \(r_{ji}=\lVert \mathbf{R}_{ji} \lVert\) is its norm. The switching function \(s(r)\) is defined as:
\[\begin{split}s(r)= \begin{cases} \frac{1}{r}, & r<r_s \\ \frac{1}{r} \{ {(\frac{r - r_s}{ r_c - r_s})}^3 (-6 {(\frac{r - r_s}{ r_c - r_s})}^2 +15 \frac{r - r_s}{ r_c - r_s} -10) +1 \}, & r_s \leq r<r_c \\ 0, & r \geq r_c \end{cases}\end{split}\]Each row of the embedding matrix \(\mathcal{G}^i \in \mathbb{R}^{N \times M_1}\) consists of outputs of a embedding network \(\mathcal{N}\) of \(s(r_{ji})\):
\[(\mathcal{G}^i)_j = \mathcal{N}(s(r_{ji}))\]\(\mathcal{G}^i_< \in \mathbb{R}^{N \times M_2}\) takes first \(M_2\) columns of \(\mathcal{G}^i\). The equation of embedding network \(\mathcal{N}\) can be found at
deepmd.tf.utils.network.embedding_net().- Parameters:
- rcut
The cut-off radius \(r_c\)
- rcut_smth
From where the environment matrix should be smoothed \(r_s\)
- 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 \(\mathcal{N}\)
- axis_neuron
Number of the axis neuron \(M_2\) (number of columns of the sub-matrix of the embedding matrix)
- 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_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.
- env_protection: float
Protection parameter to prevent division by zero errors during environment matrix calculations.
- 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.
- mixed_types()[source]
Returns if the descriptor requires a neighbor list that distinguish different atomic types or not.
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.
- abstract compute_input_stats(merged: List[dict], path: deepmd.utils.path.DPPath | None = None)[source]
Update mean and stddev for descriptor elements.
- call(coord_ext, atype_ext, nlist, mapping: numpy.ndarray | None = None)[source]
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:
descriptorThe descriptor. shape: nf x nloc x (ng x axis_neuron)
grThe rotationally equivariant and permutationally invariant single particle representation. shape: nf x nloc x ng x 3
g2The rotationally invariant pair-partical representation. this descriptor returns None
h2The rotationally equivariant pair-partical representation. this descriptor returns None
swThe smooth switch function.
- classmethod deserialize(data: dict) DescrptSeA[source]
Deserialize from dict.