@inproceedings{
cunnington2023symbolic,
title={Symbolic Learning for Material Discovery},
author={Daniel Cunnington and Flaviu Cipcigan and Rodrigo Neumann Barros Ferreira and Jonathan Booth},
booktitle={AI for Accelerated Materials Design - NeurIPS 2023 Workshop},
year={2023},
url={https://openreview.net/forum?id=m0YBr6wFin}
}
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.
Symbolic Learning for Material Discovery
Daniel Cunnington Flaviu Cipcigan Rodrigo Neumann Barros Ferreira Jonathan Booth