Multimodal foundation models for material property prediction and discovery

Artificial intelligence is transforming computational materials science by improving property prediction and accelerating the discovery of novel materials. Recently, publicly available material data repositories have grown rapidly, encompassing not only more materials but also a greater variety and quantity of their associated properties. Existing machine-learning efforts in materials science focus primarily on single-modality tasks, i.e., relationships between materials and a single physical property, thus not taking advantage of the rich multimodal data available. Here, we introduce multimodal learning for materials (MultiMat), a framework enabling self-supervised multimodal training of foundation models for materials. Using the Materials Project database, we demonstrate the potential of MultiMat by: (1) achieving state-of-the-art performance for challenging material property prediction tasks; (2) enabling novel and accurate material discovery via latent-space similarity, allowing screening for stable materials with desired properties; and (3) encoding emergent features that correlate with material properties and may provide novel scientific insights.