MoMa: A Simple Modular Learning Framework for Material Property Prediction
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.
Cite
Text
Wang et al. "MoMa: A Simple Modular Learning Framework for Material Property Prediction." International Conference on Learning Representations, 2026.Markdown
[Wang et al. "MoMa: A Simple Modular Learning Framework for Material Property Prediction." International Conference on Learning Representations, 2026.](https://mlanthology.org/iclr/2026/wang2026iclr-moma/)BibTeX
@inproceedings{wang2026iclr-moma,
title = {{MoMa: A Simple Modular Learning Framework for Material Property Prediction}},
author = {Wang, Botian and Ouyang, Yawen and Li, Yaohui and Pan, Mianzhi and Tang, Yuanhang and Cui, Haorui and Wang, Yiqun and Zhang, Jianbing and Wang, Xiaonan and Ma, Wei-Ying and Zhou, Hao},
booktitle = {International Conference on Learning Representations},
year = {2026},
url = {https://mlanthology.org/iclr/2026/wang2026iclr-moma/}
}