Publications Details

Machine learning for materials science: Barriers to broader adoption

Boyce, Brad B.; Dingreville, Remi P.; Desai, Saaketh D.; Walker, Elise; Shilt, Troy; Bassett, Kimberly L.; Wixom, Ryan R.; Stebner, Aaron P.; Arroyave, Raymundo; Hattrick-Simpers, Jason; Warren, James A.

Machine learning is on a bit of a tear right now, with advances that are infiltrating nearly every aspect of our lives. In the domain of materials science, this wave seems to be growing into a tsunami. Yet, there are still real hurdles that we face to maximize its benefit. This Matter of Opinion, crafted as a result of a workshop hosted by researchers at Sandia National Laboratories and attended by a cadre of luminaries, briefly summarizes our perspective on these barriers. By recognizing these problems in a community forum, we can share the burden of their resolution together with a common purpose and coordinated effort.