mace_omat_lora_v1

mace_omat_medium — Fine-tuned MACE OMAT for LiF

A LoRA fine-tuned MACE (Multi-Atomic Cluster Expansion) foundation model for ionic lithium–fluoride systems. This model is built on the MACE-MP-0 OMAT medium checkpoint and fine-tuned on Quantum ESPRESSO DFT data covering bulk LiF, LiF interfaces, isolated frames.

This model is directly produced by the mlpdft project.

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Model Details

Model Description

MACE (Multi-Atomic Cluster Expansion) is an equivariant message-passing neural network that constructs many-body expansions with learnable radial and angular features. It achieves state-of-the-art accuracy while maintaining speed suitable for molecular dynamics simulations of thousands of atoms.

This variant is fine-tuned via LoRA (Low-Rank Adaptation) on the MACE-MP-0 OMAT-medium foundation model to improve transfer to ionic lithium–fluoride chemistries.

• Developed by: Jorge Munoz (mlpdft project)
• Base model: MACE-MP-0 OMAT medium (mace_omat_medium)
• Model type: Equivariant message-passing neural network (E(3)-equivariant)
• Fine-tuning method: LoRA (Low-Rank Adaptation)
• License: MIT

Model Sources

• Repository: github.com/jorgemunozl/mlpdft
• Base model: MACE-MP-0
• Paper (MACE foundation): A foundation model for atomistic materials chemistry

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Training Details

Training Data

The model was fine-tuned on the jorgemunozl/minimal_li_f_mace_dataset dataset, which contains DFT calculations (Quantum ESPRESSO, PBEsol, PAW pseudopotentials) spanning 8 groups:

Each frame carries:

• REF_energy — total DFT energy (eV)
• REF_forces — per-atom forces (eV/Å)
• stress — stress tensor (when available)

Energy offsets (isolated-atom energies in eV):

RY_TO_EV ≈ 13.605698

Training Procedure

Fine-tuning hyperparameters

Training script

Fine-tuning is done via the mace_run_train CLI from the mace-torch package, driven by src/mlpdft/train.py. See the mlpdft README for full details.

uv run python src/mlpdft/train.py

Hardware

Training was performed on CUDA GPU RTX4000ADA support (device="cuda").

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Evaluation

Metrics

Evaluation metrics are computed per group on energy and forces:

Evaluation is run via:

uv run python src/mlpdft/evaluate_mace_metrics.py

The script runs the model on all groups from the dataset and prints a summary table with energy and force errors.

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Technical Specifications

Model Architecture and Objective

MACE is a body-ordered equivariant message-passing neural network:

• Input: Atomic numbers and Cartesian positions
• Embeddings: One-hot atomic number → learnable scalar features
• Message passing: Atomic cluster expansion (ACE) basis with radial Bessel basis + spherical harmonics for angular information
• Equivariance: E(3)-equivariant (rotations, translations, reflections)
• Readout: Site energies summed to total energy; forces via automatic differentiation
• Output: Total energy (eV) and per-atom forces (eV/Å)

Compute Infrastructure

• Framework: PyTorch 2.x + mace-torch ≥ 0.3.6
• Hardware requirements: CPU for inference; GPU recommended for large-scale MD

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Citation

If you use this model, please cite both the MACE foundation model and the CHGNet paper:

@article{batatia2023foundation,
      title={A foundation model for atomistic materials chemistry},
      author={Ilyes Batatia and Philipp Benner and Yuan Chiang and Alin M. Elena
              and Fabian Zills and Gábor Csányi},
      year={2023},
      eprint={2401.00096},
      archivePrefix={arXiv},
      primaryClass={physics.chem-ph}
}

@article{deng2023chgnet,
      title={CHGNet: Pretrained universal neural network potential
             for charge-informed atomistic modeling},
      author={Bowen Deng and Peichen Zhong and KyuJung Jun
              and Janosh Riebesell and Kevin Han and Christopher J. Bartel
              and Gerbrand Ceder},
      year={2023},
      eprint={2302.14231},
      archivePrefix={arXiv},
      primaryClass={cond-mat.mtrl-sci}
}

MACE-Universal by Yuan Chiang, 2023, Hugging Face, Revision e5ebd9b, DOI: 10.57967/hf/1202, URL: https://huggingface.co/cyrusyc/mace-universal

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Model Card Authors

Jorge Munoz — mlpdft project

Model Card Contact

• Hugging Face: jorgemunozl
• GitHub: github.com/jorgemunozl/mlpdft