deepmd.tf.descriptor.se_a_mask#
Classes#
DeepPot-SE constructed from all information (both angular and radial) of |
Module Contents#
- class deepmd.tf.descriptor.se_a_mask.DescrptSeAMask(sel: list[int], neuron: list[int] = [24, 48, 96], axis_neuron: int = 8, resnet_dt: bool = False, trainable: bool = True, type_one_side: bool = False, exclude_types: list[list[int]] = [], seed: int | None = None, activation_function: str = 'tanh', precision: str = 'default', uniform_seed: bool = False, tebd_input_mode: str = 'concat', **kwargs)[source]#
Bases:
deepmd.tf.descriptor.se_a.DescrptSeADeepPot-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(). Specially for descriptor se_a_mask is a concise implementation of se_a. The difference is that se_a_mask only considered a non-pbc system. And accept a mask matrix to indicate the atom i in frame j is a real atom or not. (1 means real atom, 0 means ghost atom) Thus se_a_mask can accept a variable number of atoms in a frame.- Parameters:
- sel
list[int] sel[i] specifies the maxmum number of type i atoms in the neighbor list.
- 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.
- seed
Random seed for initializing the network parameters.
- 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.
- activation_function
The activation function in the embedding net. Supported options are {0}
- precision
The precision of the embedding net parameters. Supported options are {1}
- uniform_seed
Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
- sel
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.
- compute_input_stats(data_coord: list, data_box: list, data_atype: list, natoms_vec: list, mesh: list, input_dict: dict, **kwargs) None[source]#
Compute the statisitcs (avg and std) of the training data. The input will be normalized by the statistics.
- Parameters:
- data_coord
The coordinates. Can be generated by deepmd.tf.model.make_stat_input
- data_box
The box. Can be generated by deepmd.tf.model.make_stat_input
- data_atype
The atom types. Can be generated by deepmd.tf.model.make_stat_input
- natoms_vec
The vector for the number of atoms of the system and different types of atoms. Can be generated by deepmd.tf.model.make_stat_input
- mesh
The mesh for neighbor searching. Can be generated by deepmd.tf.model.make_stat_input
- input_dict
Dictionary for additional input
- **kwargs
Additional keyword arguments.
- 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[str, Any], reuse: bool | None = None, suffix: str = '') deepmd.tf.env.tf.Tensor[source]#
Build the computational graph for the descriptor.
- Parameters:
- coord_
The coordinate of atoms
- atype_
The type of atoms
- natoms
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
- box_
tf.Tensor The box of the system
- mesh
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.
- input_dict
Dictionary for additional inputs
- reuse
The weights in the networks should be reused when get the variable.
- suffix
Name suffix to identify this descriptor
- Returns:
descriptorThe output descriptor
- prod_force_virial(atom_ener: deepmd.tf.env.tf.Tensor, natoms: deepmd.tf.env.tf.Tensor) tuple[deepmd.tf.env.tf.Tensor, deepmd.tf.env.tf.Tensor, deepmd.tf.env.tf.Tensor][source]#
Compute force and virial.
- Parameters:
- atom_ener
The atomic energy
- natoms
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
- Returns:
forceThe force on atoms
virialNone for se_a_mask op
atom_virialNone for se_a_mask op