config.json

{
  "config_schema_version": 1,
  "family_name": "aimnet2-nse",
  "ensemble_size": 4,
  "member_names": [
    "aimnet2nse_0",
    "aimnet2nse_1",
    "aimnet2nse_2",
    "aimnet2nse_3"
  ],
  "cutoff": 5.0,
  "needs_coulomb": true,
  "needs_dispersion": true,
  "coulomb_mode": "sr_embedded",
  "implemented_species": [
    1,
    5,
    6,
    7,
    8,
    9,
    14,
    15,
    16,
    17,
    33,
    34,
    35,
    53
  ],
  "library_name": "aimnet",
  "tags": [
    "chemistry",
    "molecular-dynamics",
    "force-field",
    "aimnet2"
  ],
  "model_yaml": "class: aimnet.models.aimnet2.AIMNet2\nkwargs:\n  nfeature: 16\n  d2features: true\n  ncomb_v: 12\n  hidden:\n  - - 512\n    - 380\n  - - 512\n    - 380\n  - - 512\n    - 380\n    - 380\n  aim_size: 256\n  num_charge_channels: 2\n  aev:\n    rc_s: 5.0\n    nshifts_s: 16\n  outputs:\n    energy_mlp:\n      class: aimnet.modules.Output\n      kwargs:\n        n_in: 256\n        n_out: 1\n        key_in: aim\n        key_out: energy\n        mlp:\n          activation_fn: torch.nn.GELU\n          last_linear: true\n          hidden:\n          - 128\n          - 128\n    atomic_shift:\n      class: aimnet.modules.AtomicShift\n      kwargs:\n        key_in: energy\n        key_out: energy\n    atomic_sum:\n      class: aimnet.modules.AtomicSum\n      kwargs:\n        key_in: energy\n        key_out: energy\n    srcoulomb:\n      class: aimnet.modules.SRCoulomb\n      kwargs:\n        rc: 4.199999809265137\n        key_in: charges\n        key_out: energy\n        envelope: exp\n",
  "format_version": 2,
  "d3_params": {
    "s8": 0.3908,
    "a1": 0.566,
    "a2": 3.128,
    "s6": 1.0
  },
  "coulomb_sr_rc": 4.199999809265137,
  "coulomb_sr_envelope": "exp",
  "has_embedded_lr": true
}