# Symbolic Learning for Material Discovery

arXiv: https://arxiv.org/abs/2312.11487 · HTML: https://mlsanity.com/paper/2312.11487/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [AI Feynman: a Physics-Inspired Method for Symbolic Regression](https://mlsanity.com/paper/1905.11481/) (2019)
- [Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation](https://mlsanity.com/paper/2106.04399/) (2021)
- [Interpretable models for extrapolation in scientific machine learning](https://mlsanity.com/paper/2212.10283/) (2022)
- [Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimization](https://mlsanity.com/paper/1807.01279/) (2018)
- [Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data](https://mlsanity.com/paper/2206.06422/) (2022)

## Abstract (arXiv)

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.