MoMa: A Modular Deep Learning Framework for Material Property Prediction

Deep learning methods for material property prediction have been widely explored to advance materials discovery. However, the prevailing pre-train then fine-tune paradigm often fails to address the inherent diversity and disparity of material tasks. To overcome these challenges, we introduce MoMa, a Modular framework for Materials that first trains specialized modules across a wide range of tasks and then adaptively composes synergistic modules tailored to each downstream scenario. Evaluation across 17 datasets demonstrates the superiority of MoMa, with a substantial 14% average improvement over the strongest baseline. Few-shot and continual learning experiments further highlight MoMa's potential for real-world applications. Pioneering a new paradigm of modular material learning, MoMa will be open-sourced to foster broader community collaboration.

@article{wang2025_2502.15483,
  title={ MoMa: A Modular Deep Learning Framework for Material Property Prediction },
  author={ Botian Wang and Yawen Ouyang and Yaohui Li and Yiqun Wang and Haorui Cui and Jianbing Zhang and Xiaonan Wang and Wei-Ying Ma and Hao Zhou },
  journal={arXiv preprint arXiv:2502.15483},
  year={ 2025 }
}