Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science

TL;DR

This paper discusses how to conduct impactful machine learning research in chemistry and materials science, emphasizing current applications, researcher perspectives, and strategies for maximizing scientific impact.

Contribution

It offers a comprehensive perspective on current ML applications in chemistry and provides guidelines for impactful research in the field.

Findings

Machine learning is increasingly applied in chemistry and materials science.

Current challenges limit ML's full potential in these fields.

Strategies for impactful research are proposed.

Abstract

Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it is still not reaching its full potential or maturity. In this perspective, we first outline current applications across a diversity of problems in chemistry. Then, we discuss how machine learning researchers view and approach problems in the field. Finally, we provide our considerations for maximizing impact when researching machine learning for chemistry.