deepmodelingcommunity/DPA3-Omol-Large

Model Summary

This model card provides a DPA3 model[1] trained on the OMol25[2] dataset. We provide one model:

• DPA3-Omol-Large – 12 layers, large-scale model for broad molecular chemistry

The model is trained with charge and spin as input frame parameters, following the OMol25 dataset convention. Here spin refers to the spin multiplicity (2S+1), not the spin quantum number S. Users can specify charge and spin when running simulations; if not specified, defaults of charge=0 and spin=1 (singlet) are used.

The model is compatible with DeePMD-kit v3.1.3. For other installation options, please visit the Releases page to download the off-line package for v3.1.3, and refer to the official documentation for off-line installation instructions.

Usage

Model Evaluation

Evaluate the model through the dp test command line:

dp --pt test -m DPA3-Omol-Large.pt -s path_to_your_system

ASE Calculator

You can directly use the following Python code for prediction or optimization with standard ASE calculator.

Charge and spin can be explicitly specified via fparam keyword in atoms.info. Note that spin here means spin multiplicity (2S+1). If not set, the default values charge=0 and spin=1 (singlet) will be used.

## Compute potential energy
from ase import Atoms
from deepmd.calculator import DP as DPCalculator

dp = DPCalculator("DPA3-Omol-Large.pt")

# Example: ethanol molecule
ethanol = Atoms(
    "C2H6O",
    positions=[
        (-0.7472, -0.0575, 0.0000),
        ( 0.7209,  0.0178, 0.0000),
        ( 1.1431,  1.4297, 0.0000),
        (-1.1576, -1.0720, 0.0000),
        (-1.1267,  0.4548, -0.8932),
        (-1.1267,  0.4548,  0.8932),
        ( 1.0797, -0.5050, -0.8946),
        ( 1.0797, -0.5050,  0.8946),
        ( 2.1108,  1.4520,  0.0000),
    ],
    cell=[100, 100, 100],
)

# Specify charge and spin multiplicity (optional)
# If not set, defaults are charge=0, spin=1 (singlet)
ethanol.info.update(
  {"fparam": [0.0, 1.0]} # charge=0, spin multiplicity=1 (singlet)
)

ethanol.calc = dp
print(ethanol.get_potential_energy())
print(ethanol.get_forces())

## Run BFGS structure optimization
from ase.optimize import BFGS

dyn = BFGS(ethanol)
dyn.run(fmax=1e-6)
print(ethanol.get_positions())

LAMMPS

Use LAMMPS for molecular dynamics calculation with the DPA3 model, you first need to freeze the *.pt model into a *.pth model using the following command:

dp --pt freeze -c DPA3-Omol-Large.pt -o DPA3-Omol-Large.pth

Then you can make the following modifications in the LAMMPS script to call the DeePMD-kit interface (also see potential.md).

Charge and spin are provided via the fparam keyword in the order of charge, spin (spin = spin multiplicity, 2S+1). If fparam is not specified, the default values 0.0 1.0 (charge=0, spin multiplicity=1, i.e. singlet) will be used.

# With explicit charge and spin multiplicity (e.g., charge=2, multiplicity=1)
pair_style      deepmd DPA3-Omol-Large.pth fparam 2.0 1.0
pair_coeff      * * C H O

# Without fparam: defaults to charge=0, spin multiplicity=1
pair_style      deepmd DPA3-Omol-Large.pth
pair_coeff      * * C H O

For more details on the fparam keyword, see the DeePMD-kit LAMMPS documentation.

Training Dataset

The model is trained on the Open Molecules 2025 (OMol25) dataset[2], a large-scale resource for molecular chemistry ML models introduced by Meta FAIR. OMol25 comprises over 100 million DFT single-point calculations at the ωB97M-V/def2-TZVPD level of theory.

Key characteristics of OMol25:

• 83 elements across the periodic table
• ~83M unique molecular systems, including small molecules, biomolecules, metal complexes, and electrolytes
• System sizes up to 350 atoms (50 on average)
• Diverse charge states (−10 to +10) and spin multiplicities (1 to 11)
• Explicit solvation, conformers, and reactive structures

The dataset is organized into four major domains:

• Biomolecules: protein–ligand, protein–protein, and nucleic acid interactions extracted from BioLiP2 and other structural databases, sampled via classical MD
• Metal Complexes: diverse monometallic transition metal, main group metal, and lanthanide systems with varied ligands and spin states, generated using the Architector package
• Electrolytes: aqueous and non-aqueous solutions, ionic liquids, and molten salts, sampled via MD (including Ring Polymer MD for nuclear quantum effects) and electrolyte reactivity networks
• Community: recomputed existing datasets (ANI-2X, Transition-1X, SPICE2, GEOM, etc.) at consistent ωB97M-V/def2-TZVPD level of theory, plus interpolated reactivity datasets

Compositional splitting ensures that validation and test sets contain out-of-distribution molecular formulas relative to training data.

Training Details

We train the DPA3 model in its large (12-layer) configuration, truncated within LiGS order 2.

Model configuration

Parameter	Value
n_dim	256
e_dim	256
a_dim	256
nlayers	12

Training setup

• Engine: DeePMD-kit (v3.1.0 required)
• Batch size: auto:2048 (DeePMD-kit automatic batchsize)
• Hardware: 32 × NVIDIA A800 GPUs
• Training steps: 2 million steps
• Learning rate schedule: Cosine annealing
• Cutoff radii and neighbor selections:
  • e_rcut = 6.0, e_rcut_smth = 5.3, e_sel = 30
  • a_rcut = 4.5, a_rcut_smth = 4.0, a_sel = 15

Other hyperparameters and training details can be found in the DPA3 paper[1].

Performance

Accuracy on OMol25 Validation Set

We report energy and force errors on the OMol25 validation set. All values are in meV (energy per atom) and meV/Å (force).

Model	Energy MAE/atom (meV)	Energy RMSE/atom (meV)	Force MAE (meV/Å)	Force RMSE (meV/Å)
MACE-OMol-L	1.917	11.727	10.690	63.754
DPA3-Omol-Large	1.328	11.347	12.362	62.934

Accuracy on LAMBench

We evaluate DPA3-Omol-Large on molecule property calculation tasks from LAMBench[3]. The following results compare DPA3-Omol-Large (ours) with DPA-3.2-5M and MACE-OMol-L.

Ligand Binding

Model	RMSE (kcal/mol)	MAE (kcal/mol)
DPA-3.2-5M	3.40	6.90
MACE-OMol-L	1.75	0.84
DPA3-Omol-Large	1.29	0.65

TorsionNet500

Model	MAEB (kcal/mol)	MAE (kcal/mol)	RMSE (kcal/mol)	NAHB_h
DPA-3.2-5M	0.47	0.29	0.43	50
MACE-OMol-L	0.23	0.14	0.23	9
DPA3-Omol-Large	0.24	0.16	0.25	13

Wiggle150

Model	RMSE (kcal/mol)	MAE (kcal/mol)
DPA-3.2-5M	1.54	1.19
MACE-OMol-L	1.18	0.89
DPA3-Omol-Large	1.25	0.94

Reaction Barrier

Model	RMSE (kcal/mol)	MAE (kcal/mol)
DPA-3.2-5M	12.37	6.30
MACE-OMol-L	3.53	2.12
DPA3-Omol-Large	12.42	3.36

Reference

[1] Duo Zhang, Anyang Peng, Chun Cai, Wentao Li, Yuanchang Zhou, Jinzhe Zeng, Mingyu Guo et al. "A Graph Neural Network for the Era of Large Atomistic Models." arXiv preprint arXiv:2506.01686 (2025).

[2] Daniel S. Levine, Muhammed Shuaibi, Evan Walter Clark Spotte-Smith, Michael G. Taylor, Muhammad R. Hasyim, Kyle Michel, Ilyes Batatia et al. "The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models." arXiv preprint arXiv:2505.08762 (2025).

[3] Anyang Peng, Chun Cai, Mingyu Guo, Duo Zhang, Chengqian Zhang, Wanrun Jiang, Yinan Wang et al. "LAMBench: a benchmark for large atomistic models." npj Computational Materials (2026).