Poster
in
Workshop: AI for Accelerated Materials Design (AI4Mat-2023)
Symbolic Learning for Material Discovery
Daniel Cunnington · Flaviu Cipcigan · Rodrigo Neumann Barros Ferreira · Jonathan Booth
Keywords: [ Symbolic Learning ] [ mof ] [ material discovery ] [ Active Learning ] [ active learning ] [ symbolic learning ]
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.