How to do impactful research in artificial intelligence for chemistry and materials science

Machine learning (ML) has been applied in many facets of chemistry, and its use is rapidly growing. We argue in this perspective that despite this dramatic growth and impact, ML could be employed better and more extensively. Current work is still far from exhausting the potential of ML to advance theory and application in chemistry in terms of breadth, depth, and scale. In addition, the actual types of problems that ML could tackle, such as hypothesis generation or enabling internalized scientific understanding, are still areas of active research or open problems.