用多模态学习加速新材料逆向设计，高效发现目标性能材料
MEIDNet: Multimodal generative AI framework for inverse materials design
- 融合对比学习与等变图神经网络，联合建模结构与性能
- 隐空间对齐度达0.96，生成13.6%稳定新钙钛矿结构
- 适合材料逆向设计、高通量筛选及跨模态学习研究者
本文提出多模态等变逆向设计网络（MEIDNet），通过对比学习联合学习材料结构信息与性能，利用等变图神经网络（EGNN）编码结构。结合生成式逆向设计与多模态学习，该方法加速化学-结构空间探索，实现满足预设性能目标的材料发现。通过跨模态学习融合三种模态，MEIDNet在隐空间对齐度达到0.96。采用课程学习策略后，训练效率比传统方法提升约60倍。在生成低带隙钙钛矿结构方面，实现13.6%的稳定、独特且新颖（SUN）率，经从头算方法验证。该框架具备良好可扩展性与适应性，为跨模态化学空间通用学习提供可能。
原文摘要 · Abstract (English)
In this work, we present Multimodal Equivariant Inverse Design Network (MEIDNet), a framework that jointly learns structural information and materials properties through contrastive learning, while encoding structures via an equivariant graph neural network (EGNN). By combining generative inverse design with multimodal learning, our approach accelerates the exploration of chemical-structural space and facilitates the discovery of materials that satisfy predefined property targets. MEIDNet exhibits strong latent-space alignment with cosine similarity 0.96 by fusion of three modalities through cross-modal learning. Through implementation of curriculum learning strategies, MEIDNet achieves ~60 times higher learning efficiency than conventional training techniques. The potential of our multimodal approach is demonstrated by generating low-bandgap perovskite structures at a stable, unique, and novel (SUN) rate of 13.6 %, which are further validated by ab initio methods. Our inverse design framework demonstrates both scalability and adaptability, paving the way for the universal learning of chemical space across diverse modalities.
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