MEIDNet: Multimodal generative AI framework for inverse materials design
This work addresses the challenge of discovering materials with predefined properties for researchers in materials science, representing a novel method rather than an incremental improvement.
The paper tackles the problem of inverse materials design by developing MEIDNet, a multimodal generative AI framework that accelerates exploration of chemical-structural space, achieving a 60 times higher learning efficiency and generating low-bandgap perovskite structures at a 13.6% SUN rate.
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.