Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science
This is a perspective paper offering guidance for researchers in AI for chemistry and materials science, focusing on improving impact rather than introducing new methods.
The paper outlines current applications of machine learning in chemistry and materials science, discusses how researchers approach problems in the field, and provides considerations for maximizing research impact, without presenting specific results or numbers.
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.