Symbolic Learning for Material Discovery

• URL: http://arxiv.org/abs/2312.11487v1
• Date: Thu, 30 Nov 2023 15:56:00 GMT
• Title: Symbolic Learning for Material Discovery
• Authors: Daniel Cunnington, Flaviu Cipcigan, Rodrigo Neumann Barros Ferreira, Jonathan Booth
• Abstract summary: SyMDis is a sample efficient optimisation method based on symbolic learning. SyMDis performs comparably to a state-of-the-art optimiser, whilst learning interpretable rules to aid physical and chemical verification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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