# 3.1. 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 model section of the input.json, for example,

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


The two subsections, descriptor and fitting_net, define the descriptor and the fitting net, respectively.

The type_map is optional, which provides the element names (but not necessarily same with the actual name of the element) of the corresponding atom types. A model for water, 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 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. loc_frame: Defines a local frame at each atom, and the compute the descriptor as local coordinates under this frame.

5. hybrid: Concate a list of descriptors to form a new descriptor.

The fitting of the following physical properties are 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.