Symbolic Learning for Material Discovery
Autor: | Cunnington, Daniel, Cipcigan, Flaviu, Ferreira, Rodrigo Neumann Barros, Booth, Jonathan |
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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 |
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