Multimodal Foundation Models for Material Property Prediction and Discovery
This work addresses the need for more efficient and accurate computational methods in materials science, offering a novel approach that leverages multimodal data to improve predictions and accelerate discovery, though it is incremental in advancing existing machine learning techniques in this domain.
The paper tackles the problem of predicting material properties and discovering new materials by introducing Multimodal Learning for Materials (MultiMat), a framework for self-supervised multi-modality training of foundation models, which achieves state-of-the-art performance on challenging prediction tasks and enables accurate material discovery.
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.