# Deep Potential - Range Correction (DPRc)

Deep Potential - Range Correction (DPRc) is designed to combine with QM/MM method, and corrects energies from a low-level QM/MM method to a high-level QM/MM method:

$$ E=E_\text{QM}(\mathbf R; \mathbf P)  + E_\text{QM/MM}(\mathbf R; \mathbf P) + E_\text{MM}(\mathbf R) + E_\text{DPRc}(\mathbf R) $$

See the [JCTC paper](https://doi.org/10.1021/acs.jctc.1c00201) for details.

## Training data

Instead the normal _ab initio_ data, one needs to provide the correction from a low-level QM/MM method to a high-level QM/MM method:

$$ E = E_\text{high-level QM/MM} - E_\text{low-level QM/MM} $$

Two levels of data use the same MM method, so $E_\text{MM}$ is eliminated.

## Training the DPRc model

In a DPRc model, QM atoms and MM atoms have different atom types. Assuming we have 4 QM atom types (C, H, O, P) and 2 MM atom types (HW, OW):

```json
"type_map": ["C", "H", "HW", "O", "OW", "P"]
```

As described in the paper, the DPRc model only corrects $E_\text{QM}$ and $E_\text{QM/MM}$ within the cutoff, so we use a hybrid descriptor to describe them separatedly:

```json
"descriptor" :{
    "type":             "hybrid",
    "list" : [
        {
            "type":     "se_e2_a",
            "sel":              [6, 11, 0, 6, 0, 1],
            "rcut_smth":        1.00,
            "rcut":             9.00,
            "neuron":           [12, 25, 50],
            "exclude_types":    [[2, 2], [2, 4], [4, 4], [0, 2], [0, 4], [1, 2], [1, 4], [3, 2], [3, 4], [5, 2], [5, 4]],
            "axis_neuron":      12,
            "set_davg_zero":    true,
            "_comment": " QM/QM interaction"
        },
        {
            "type":     "se_e2_a",
            "sel":              [6, 11, 100, 6, 50, 1],
            "rcut_smth":        0.50,
            "rcut":             6.00,
            "neuron":           [12, 25, 50],
            "exclude_types":    [[0, 0], [0, 1], [0, 3], [0, 5], [1, 1], [1, 3], [1, 5], [3, 3], [3, 5], [5, 5], [2, 2], [2, 4], [4, 4]],
            "axis_neuron":      12,
            "set_davg_zero":    true,
            "_comment": " QM/MM interaction"
        }
    ]
}
```

{ref}`exclude_types <model/descriptor[se_e2_a]/exclude_types>` can be generated by the following Python script:
```py
from itertools import combinations_with_replacement, product
qm = (0, 1, 3, 5)
mm = (2, 4)
print("QM/QM:", list(map(list, list(combinations_with_replacement(mm, 2)) + list(product(qm, mm)))))
print("QM/MM:", list(map(list, list(combinations_with_replacement(qm, 2)) + list(combinations_with_replacement(mm, 2)))))
```

Also, DPRc assumes MM atom energies ({ref}`atom_ener <model/fitting_net[ener]/atom_ener>`) are zero:

```json
"fitting_net": {
   "neuron": [240, 240, 240],
   "resnet_dt": true,
   "atom_ener": [null, null, 0.0, null, 0.0, null]
}
```

Note that {ref}`atom_ener <model/fitting_net[ener]/atom_ener>` only works when {ref}`descriptor/set_davg_zero <model/descriptor[se_e2_a]/set_davg_zero>` is `true`.

## Run MD simulations

The DPRc model has the best practices with the [AMBER](../third-party/out-of-deepmd-kit.md#amber-interface-to-deepmd-kit) QM/MM module. An example is given by [GitLab RutgersLBSR/AmberDPRc](https://gitlab.com/RutgersLBSR/AmberDPRc/). In theory, DPRc is able to be used with any QM/MM package, as long as the DeePMD-kit package accepts QM atoms and MM atoms within the cutoff range and returns energies and forces.