Symbolic Learning for Material Discovery

• DOI: 10.48550/arxiv.2312.11487
• Publication Date: 2023-01-01
• Authors: Cunnington, Daniel; Cipcigan, Flaviu; Ferreira, Rodrigo...; Booth, Jonathan
• Keywords: FOS: Computer and information sciences; Condensed Matter - Materials Science; Computer Science - Machine Learning; Materials Science (cond-mat.mtrl-sci); FOS: Physical sciences; Machine Learning (cs.LG)

Accepted at the AI for Accelerated Materials Discovery Workshop, NeurIPS2023<br/>Discovering new materials is essential to solve challenges in climate change, sustainability and healthcare. A typical task in materials discovery is to search for a material in a database which maximises the value of a function. That function is often expensive to evaluate, and can rely upon a simulation or an experiment. Here, we introduce SyMDis, a sample efficient optimisation method based on symbolic learning, that discovers near-optimal materials in a large database. SyMDis performs comparably to a state-of-the-art optimiser, whilst learning interpretable rules to aid physical and chemical verification. Furthermore, the rules learned by SyMDis generalise to unseen datasets and return high performing candidates in a zero-shot evaluation, which is difficult to achieve with other approaches.<br/>