4.7. Descriptor "hybrid"
Note
Supported backends: TensorFlow , PyTorch , DP
This descriptor hybridizes multiple descriptors to form a new descriptor. For example, we have a list of descriptors denoted by \(\mathcal D_1\), \(\mathcal D_2\), …, \(\mathcal D_N\), the hybrid descriptor this the concatenation of the list, i.e. \(\mathcal D = (\mathcal D_1, \mathcal D_2, \cdots, \mathcal D_N)\).
4.7.1. Theory
A hybrid descriptor \(\mathcal{D}^i_\text{hyb}\) concatenates multiple kinds of descriptors into one descriptor:
The list of descriptors can be different types or the same descriptors with different parameters. This way, one can set the different cutoff radii for different descriptors.[1]
4.7.2. Instructions
To use the descriptor in DeePMD-kit, one firstly set the type to hybrid, then provide the definitions of the descriptors by the items in the list
,
"descriptor" :{
"type": "hybrid",
"list" : [
{
"type" : "se_e2_a",
...
},
{
"type" : "se_e2_r",
...
}
]
},
A complete training input script of this example can be found in the directory
$deepmd_source_dir/examples/water/hybrid/input.json