Symbolic Learning for Material Discovery

Autor: Cunnington, Daniel, Cipcigan, Flaviu, Ferreira, Rodrigo Neumann Barros, Booth, Jonathan
Rok vydání: 2023
Předmět:
Druh dokumentu: Working Paper
Popis: 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.
Comment: Accepted at the AI for Accelerated Materials Discovery Workshop, NeurIPS2023
Databáze: arXiv