  Recently there has been an ever-increasing trend in the use of machine
learning (ML) and artificial intelligence (AI) methods by the materials
science, condensed matter physics, and chemistry communities. This perspective
article identifies key scientific, technical, and social opportunities that the
materials community must prioritize to consistently develop and leverage
Scientific AI to provide a credible path towards the advancement of current
materials-limited technologies. Here we highlight the intersections of these
opportunities with a series of proposed paths forward. The opportunities are
roughly 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 from
developing new infrastructure and capabilities to deploying them in industry
and academia. We provide a brief introduction to AI in materials science and
engineering, followed by detailed discussions of each of the opportunities and
paths forward.
