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.
View on arXiv@article{moro2025_2312.00111,
title={ Multimodal Foundation Models for Material Property Prediction and Discovery },
author={ Viggo Moro and Charlotte Loh and Rumen Dangovski and Ali Ghorashi and Andrew Ma and Zhuo Chen and Samuel Kim and Peter Y. Lu and Thomas Christensen and Marin Soljačić },
journal={arXiv preprint arXiv:2312.00111},
year={ 2025 }
}