Symbolic Learning for Material Discovery

Daniel Cunnington, Flaviu Cipcigan, Rodrigo Neumann Barros Ferreira, Jonathan Booth

Submission Track: Papers

Submission Category: AI-Guided Design + Automated Material Characterization

Keywords: symbolic learning, active learning, material discovery, mof

TL;DR: Discovering a near-optimal material in a large database with few in-silico simulations

Abstract: 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.

Submission Number: 17