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isayevlab/
aimnet2-2025

Name: aimnet2-2025
Availability: InStock

aimnet

chemistry

computational-chemistry

molecular-dynamics

interatomic-potential

neural-network-potential

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AIMNet2 2025 (B97-3c)

AIMNet2 is a neural network interatomic potential for fast and accurate molecular simulations. This model is trained at the B97-3c level of theory with improved intermolecular interactions. Supersedes the original aimnet2-b973c model.

Recommended for systems where intermolecular interactions are important (clusters, solvation, crystal packing).

Highlights

Fast: Orders of magnitude faster than DFT, with optional torch.compile for ~5x additional speedup on GPU
Accurate: Near-DFT accuracy for energies, forces, and charges
Broad coverage: Supports 14 elements: H, B, C, N, O, F, Si, P, S, Cl, As, Se, Br, I
Ensemble: 4 ensemble members for uncertainty estimation
Features: Energy, forces, charges, Hessian, stress tensor, periodic boundary conditions

Installation

pip install "aimnet[hf]"

Quick Start

python

from aimnet.calculators import AIMNet2Calculator

# Load from Hugging Face (downloads and caches automatically)
calc = AIMNet2Calculator("isayevlab/aimnet2-2025")

# Single-point calculation
results = calc(
    {"coord": coords, "numbers": atomic_numbers, "charge": 0.0},
    forces=True,
)
print(results["energy"])   # Energy in eV
print(results["forces"])   # Forces in eV/A
print(results["charges"])  # Partial charges in e

With ASE

python

from aimnet.calculators.aimnet2ase import AIMNet2ASE
from ase.build import molecule

atoms = molecule("H2O")
atoms.calc = AIMNet2ASE("isayevlab/aimnet2-2025")

energy = atoms.get_potential_energy()
forces = atoms.get_forces()

Ensemble Members

This repo contains 4 ensemble members (ensemble_0.safetensors through ensemble_3.safetensors). The default loads member 0. For uncertainty estimation, load all 4 and average predictions.

File	Description
`ensemble_0.safetensors`	Ensemble member 0 (default)
`ensemble_1.safetensors`	Ensemble member 1
`ensemble_2.safetensors`	Ensemble member 2
`ensemble_3.safetensors`	Ensemble member 3
`config.json`	Shared model configuration and metadata

Model Details

Architecture: AIMNet2 (Atomic Environment Vectors + message-passing MLPs)
Cutoff radius: 5.0 A
DFT functional: B97-3c
Dispersion: D3BJ (externalized, handled by calculator)
Coulomb: Short-range embedded + external long-range (DSF/Ewald)
Format: safetensors (converted from PyTorch state dict)

Limitations

Periodic systems are supported through the Python API / ASE interface, not through the Gradio demo
Element coverage is limited to the 14 elements listed above; unsupported elements will raise an error

Citation

bibtex

@article{anstine2025aimnet2,
  title={AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs},
  author={Anstine, Dylan and Zubatyuk, Roman and Isayev, Olexandr},
  journal={Chemical Science},
  year={2025},
  publisher={Royal Society of Chemistry},
  doi={10.1039/D4SC08572H}
}

License

MIT License