PolyMicros: Bootstrapping a Foundation Model for Polycrystalline Material Structure
This addresses the problem of limited data for structural materials like polycrystalline materials, enabling broader applications in industrial and scientific domains, though it is incremental in applying existing methods to a new data type.
The paper tackles the challenge of learning from hyper-sparse data in materials science by introducing a physics-driven data augmentation scheme that generates large-scale datasets from as few as five observations, resulting in PolyMicros, the first foundation model for polycrystalline materials, which zero-shot solves several 3D experimental microscopy challenges.
Recent advances in Foundation Models for Materials Science are poised to revolutionize the discovery, manufacture, and design of novel materials with tailored properties and responses. Although great strides have been made, successes have been restricted to materials classes where multi-million sample data repositories can be readily curated (e.g., atomistic structures). Unfortunately, for many structural and functional materials (e.g., mesoscale structured metal alloys), such datasets are too costly or prohibitive to construct; instead, datasets are limited to very few examples. To address this challenge, we introduce a novel machine learning approach for learning from hyper-sparse, complex spatial data in scientific domains. Our core contribution is a physics-driven data augmentation scheme that leverages an ensemble of local generative models, trained on as few as five experimental observations, and coordinates them through a novel diversity curation strategy to generate a large-scale, physically diverse dataset. We utilize this framework to construct PolyMicros, the first Foundation Model for polycrystalline materials (a structural material class important across a broad range of industrial and scientific applications). We demonstrate the utility of PolyMicros by zero-shot solving several long standing challenges related to accelerating 3D experimental microscopy. Finally, we make both our models and datasets openly available to the community.