MoMa: A Simple Modular Learning Framework for Material Property Prediction
Botian Wang, Yawen Ouyang, Yaohui Li, Mianzhi Pan, yuanhang tang, Haorui Cui, Yiqun Wang, Jianbing Zhang
Abstract
Deep learning methods for material property prediction have been widely explored to advance materials discovery. However, the prevailing pre-train paradigm often fails to address the inherent diversity and disparity of material tasks. To overcome these challenges, we introduce MoMa, a simple 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 module scaling 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.
BibTeX
@inproceedings{
wang2026moma,
title={MoMa: A Simple Modular Learning Framework for Material Property Prediction},
author={Botian Wang and Yawen Ouyang and Yaohui Li and Mianzhi Pan and yuanhang tang and Haorui Cui and Yiqun Wang and Jianbing Zhang and Xiaonan Wang and Wei-Ying Ma and Hao Zhou},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=jiSt3M25TP}
}