overall.md

Overall

A model has two parts, a descriptor that maps atomic configuration to a set of symmetry invariant features, and a fitting net that takes descriptor as input and predicts the atomic contribution to the target physical property. It's defined in the {ref}model <model> section of the input.json, for example,

    "model": {
        "type_map":	["O", "H"],
        "descriptor" :{
            "...": "..."
        },
        "fitting_net" : {
            "...": "..."
        }
    }

The two subsections, {ref}descriptor <model/descriptor> and {ref}fitting_net <model/fitting_net>, define the descriptor and the fitting net, respectively.

The {ref}type_map <model/type_map> is optional, which provides the element names (but not necessarily same as the actual name of the element) of the corresponding atom types. A water model, as in this example, has two kinds of atoms. The atom types are internally recorded as integers, e.g., 0 for oxygen and 1 for hydrogen here. A mapping from the atom type to their names is provided by {ref}type_map <model/type_map>.

DeePMD-kit implements the following descriptors:

1. se_e2_a: DeepPot-SE constructed from all information (both angular and radial) of atomic configurations. The embedding takes the distance between atoms as input.
2. se_e2_r: DeepPot-SE constructed from radial information of atomic configurations. The embedding takes the distance between atoms as input.
3. se_e3: DeepPot-SE constructed from all information (both angular and radial) of atomic configurations. The embedding takes angles between two neighboring atoms as input.
4. se_a_mask: DeepPot-SE constructed from all information (both angular and radial) of atomic configurations. The input frames in one system can have a varied number of atoms. Input particles are padded with virtual particles of the same length.
5. loc_frame: Defines a local frame at each atom and compute the descriptor as local coordinates under this frame.
6. hybrid: Concate a list of descriptors to form a new descriptor.

The fitting of the following physical properties is supported

1. ener: Fit the energy of the system. The force (derivative with atom positions) and the virial (derivative with the box tensor) can also be trained.
2. dipole: The dipole moment.
3. polar: The polarizability.