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:
See the JCTC paper for details.
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:
Two levels of data use the same MM method, so
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):
"type_map": ["C", "H", "HW", "O", "OW", "P"]As described in the paper, the DPRc model only corrects
"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:
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:
"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.
The DPRc model has the best practices with the AMBER QM/MM module. An example is given by GitLab 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.
If one wants to correct from a low-level method into a full DFT level, and the system is too large to do full DFT calculation, one may try the experimental pairwise DPRc model.
In a pairwise DPRc model, the total energy is divided into QM internal energy and the sum of QM/MM energy for each MM residue
In this way, the interaction between the QM region and each MM fragmentation can be computed and trained separately.
Thus, the pairwise DPRc model is divided into two sub-DPRc models.
qm_model is for the QM internal interaction and qmmm_model is for the QM/MM interaction.
The configuration for these two models is similar to the non-pairwise DPRc model.
It is noted that the se_atten descriptor should be used, as it is the only descriptor to support the mixed type.
{
"model": {
"type": "pairwise_dprc",
"type_map": [
"C",
"P",
"O",
"H",
"OW",
"HW"
],
"type_embedding": {
"neuron": [
8
],
"precision": "float32"
},
"qm_model": {
"descriptor": {
"type": "se_atten",
"stripped_type_embedding": true,
"sel": 24,
"rcut_smth": 0.50,
"rcut": 9.00,
"attn_layer": 0,
"neuron": [
25,
50,
100
],
"resnet_dt": false,
"axis_neuron": 12,
"precision": "float32",
"seed": 1
},
"fitting_net": {
"type": "ener",
"neuron": [
240,
240,
240
],
"resnet_dt": true,
"precision": "float32",
"atom_ener": [
null,
null,
null,
null,
0.0,
0.0
],
"seed": 1
}
},
"qmmm_model": {
"descriptor": {
"type": "se_atten",
"stripped_type_embedding": true,
"sel": 27,
"rcut_smth": 0.50,
"rcut": 6.00,
"attn_layer": 0,
"neuron": [
25,
50,
100
],
"resnet_dt": false,
"axis_neuron": 12,
"set_davg_zero": true,
"exclude_types": [
[
0,
0
],
[
0,
1
],
[
0,
2
],
[
0,
3
],
[
1,
1
],
[
1,
2
],
[
1,
3
],
[
2,
2
],
[
2,
3
],
[
3,
3
],
[
4,
4
],
[
4,
5
],
[
5,
5
]
],
"precision": "float32",
"seed": 1
},
"fitting_net": {
"type": "ener",
"neuron": [
240,
240,
240
],
"resnet_dt": true,
"seed": 1,
"precision": "float32",
"atom_ener": [
0.0,
0.0,
0.0,
0.0,
0.0,
0.0
]
}
}
}
}The pairwise model needs information for MM residues.
The model uses aparam with the shape of nframes x natoms to get the residue index.
The QM residue should always use 0 as the index.
For example, 0 0 0 1 1 1 2 2 2 means these 9 atoms are grouped into one QM residue and two MM residues.