4.3. Descriptor "se_e2_r"
Note
Supported backends: TensorFlow , PyTorch , DP
The notation of The descriptor, using either radial-only information, is given by where \(N_c\) is the expected maximum number of neighboring atoms, which is the same constant for all atoms over all frames. A matrix with a dimension of \(N_c\) will be padded if the number of neighboring atoms is less than \(N_c\). Each row of the embedding matrix \(\mathcal{G}^i \in \mathbb{R}^{N_c \times M}\) consists of \(M\) nodes from the output layer of an NN function \(\mathcal{N}_ {g}\) of \(s(r_{ij})\): where \(\boldsymbol{r}_ {ij}=\boldsymbol{r}_ j-\boldsymbol{r}_ i = (x_{ij}, y_{ij}, z_{ij})\) is the relative coordinate and \(r_{ij}=\lVert \boldsymbol{r}_{ij} \lVert\) is its norm. The switching function \(s(r)\) is defined as where \(x=\frac{r - r_s}{ r_c - r_s}\) switches from 1 at \(r_s\) to 0 at the cutoff radius \(r_c\). The switching function \(s(r)\) is smooth in the sense that the second-order derivative is continuous. In the above equations, the network parameters are not explicitly written. \(r_s\), \(r_c\) and \(M\) are hyperparameters provided by the user. The DeepPot-SE is continuous up to the second-order derivative in its domain.[1] A complete training input script of this example can be found in the directory The training input script is very similar to that of The type of the descriptor is set by the key type.se_e2_r
is short for the Deep Potential Smooth Edition (DeepPot-SE) constructed from the radial information of atomic configurations. The e2
stands for the embedding with two-atom information.4.3.1. Theory
4.3.2. Instructions
$deepmd_source_dir/examples/water/se_e2_r/input.json
se_e2_a
. The only difference lies in the descriptor section "descriptor": {
"type": "se_e2_r",
"sel": [46, 92],
"rcut_smth": 0.50,
"rcut": 6.00,
"neuron": [5, 10, 20],
"type_one_side": true,
"resnet_dt": false,
"seed": 1,
"_comment": " that's all"
},