  Artificial intelligence is transforming computational materials science,
improving the prediction of material properties, and accelerating the discovery
of novel materials. Recently, publicly available material data repositories
have grown rapidly. This growth encompasses 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 and multimodal set of material properties. Here,
we introduce Multimodal Learning for Materials (MultiMat), which enables
self-supervised multi-modality training of foundation models for materials. We
demonstrate our framework's potential using data from the Materials Project
database on multiple axes: (i) MultiMat achieves state-of-the-art performance
for challenging material property prediction tasks; (ii) MultiMat enables novel
and accurate material discovery via latent space similarity, enabling screening
for stable materials with desired properties; and (iii) MultiMat encodes
interpretable emergent features that may provide novel scientific insights.
