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arXiv · 2502.15483

MoMa: A Modular Deep Learning Framework for Material Property Prediction

Botian Wang·Yawen Ouyang·Yaohui Li·Mianzhi Pan·Yuanhang Tang·Yiqun Wang·Haorui Cui·Jianbing Zhang·Xiaonan Wang·Wei-Ying Ma·Hao Zhou
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Abstract

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.

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Botian Wang, Yawen Ouyang, Yaohui Li, Mianzhi Pan, Yuanhang Tang, Yiqun Wang, Haorui Cui, Jianbing Zhang, Xiaonan Wang, Wei-Ying Ma, Hao Zhou. 2025-02-21. MoMa: A Modular Deep Learning Framework for Material Property Prediction. https://arxiv.org/abs/2502.15483

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