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Recently there has been an ever-increasing trend in the use of machinelearning (ML) and artificial intelligence (AI) methods by the materialsscience, condensed matter physics, and chemistry communities. This perspectivearticle identifies key scientific, technical, and social opportunities that thematerials community must prioritize to consistently develop and leverageScientific AI to provide a credible path towards the advancement of currentmaterials-limited technologies. Here we highlight the intersections of theseopportunities with a series of proposed paths forward. The opportunities areroughly sorted from scientific/technical (e.g., development of robust,physically meaningful multiscale material representations) to social (e.g.,promoting an AI-ready workforce). The proposed paths forward range fromdeveloping new infrastructure and capabilities to deploying them in industryand academia. We provide a brief introduction to AI in materials science andengineering, followed by detailed discussions of each of the opportunities andpaths forward.

Scientific AI in materials science: a path to a sustainable and scalable paradigm