MolCryst-MLIPs

Molecular Crystals Database for Machine Learning Interatomic Potentials

Fine-tuned MACE models for polymorphic molecular crystals, trained using the AMLP framework.

Models

Compound	CSD Code	Energy MAE (meV/atom)	Force MAE (meV/Å)	Model	Dataset
Resorcinol	RESORA	1.568	3.903	↓ model	↓ train ↓ valid
Durene	DURENE	1.647	5.193	↓ model	↓ train ↓ valid
Coumarin	COUMAR	1.670	4.296	↓ model	↓ train ↓ valid
Benzamide	BZAMID	0.713	8.786	↓ model	↓ train ↓ valid
Niacinamide	NICOAM	1.513	7.207	↓ model	↓ train ↓ valid
Nicotinamide	NICOAC	1.201	5.824	↓ model	↓ train ↓ valid
Isonicotinamide	EHOWIH	1.912	10.809	↓ model	↓ train ↓ valid
Pyrazinamide	PYRIZIN	1.634	6.732	↓ model	↓ train ↓ valid
Benzoic acid	BENZAC	1.329	7.897	↓ model	↓ train ↓ valid
Acridine	ACRDIN	3.700	8.300	↓ model	↓ train ↓ valid
Mean		1.689	6.895

Training Protocol

• Foundation Model: MACE-MH1 (mace-mh-1-omol-1%)
• Reference Data: DFT (PBE-D4) optimizations + AIMD trajectories (25-500K)
• DFT Settings: VASP, 650 eV cutoff, EDIFF = 10⁻⁷ eV

Two-stage training:

1. Initial: LR = 2×10⁻³, energy weight = 100, force weight = 10
2. SWA (epoch 200+): LR = 5×10⁻⁵, force weight = 100

Early stopping with patience = 75 epochs. All models trained in float64.

Validation

All models validated for:

• Energy conservation: NVE drift < 10⁻⁵ over 25 ps
• Structural integrity: RDFs and P₂ order parameters preserved

Usage

We recommend using the AMLP-Analysis module (amlpa.py) for running simulations with these models:

python3 amlpa.py structure.xyz config.yaml

In your config.yaml, point to the downloaded model:

model_paths:
  - 'path/to/model.model'
device: 'gpu'
gpus: ['cuda:0']

Alternatively, you can use the models directly via the MACE calculator:

from mace.calculators import MACECalculator
calc = MACECalculator(model_paths="path/to/model.model", device="cuda")
atoms.calc = calc

For full configuration options (MD, geometry optimization, RDF analysis, etc.), refer to the AMLP documentation.

Citation

If you use these models, please cite:

@article{lahouari2026molcryst,
  title={MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals},
  author={Lahouari, Adam and Ai, Shen and Han, Jihye and Hoffstadt, Jillian and Hoellmer, Philipp and Infante, Charlotte and Jain, Pulkita and Kadam, Sangram and Martirossyan, Maya M and McCune, Amara and others},
  journal={arXiv preprint arXiv:2604.13897},
  year={2026}
}

@article{lahouari2026amlp,
  title={Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training Machine-Learned Interatomic Potentials},
  author={Lahouari, Adam and Rogal, Jutta and Tuckerman, Mark E.},
  journal={Journal of Chemical Theory and Computation},
  volume={22},
  number={1},
  pages={305--317},
  year={2026},
  publisher={American Chemical Society},
  doi={10.1021/acs.jctc.5c01610},
  url={https://doi.org/10.1021/acs.jctc.5c01610}
}

License

MIT License

Acknowledgments

• MACE development team
• NYU High Performance Computing