  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.
