Symbolic Learning for Material Discovery

TL;DR

SyMDis is a symbolic learning-based optimization method that efficiently discovers high-performing materials, providing interpretable rules that generalize well and facilitate physical and chemical verification.

Contribution

Introduces SyMDis, a novel symbolic learning approach for material discovery that is sample-efficient, interpretable, and capable of zero-shot generalization to unseen datasets.

Findings

Performs comparably to state-of-the-art optimizers.

Learns interpretable rules for material verification.

Generalizes to unseen datasets with high-quality candidates.

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.