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.

翻译：本文提出了一种跨模态等变逆向设计网络（MEIDNet）框架，该框架通过对比学习联合学习结构信息与材料性质，并利用等变图神经网络（EGNN）对结构进行编码。通过将生成式逆向设计与跨模态学习相结合，我们的方法加速了化学-结构空间的探索，并促进了满足预设性质目标的材料发现。MEIDNet通过跨模态学习融合三种模态，在潜在空间中展现出高度一致性，余弦相似度达到0.96。通过实施课程学习策略，MEIDNet的学习效率比传统训练技术提升约60倍。我们通过生成低带隙钙钛矿结构验证了该跨模态方法的潜力，其稳定、独特且新颖（SUN）的生成率达到13.6%，并进一步通过第一性原理计算得到验证。该逆向设计框架兼具可扩展性与适应性，为跨多种模态的通用化学空间学习开辟了新途径。