DOE OSTI · 2311546
Machine learning for materials science: Barriers to broader adoption
Abstract
We report 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.
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Boyce, Brad, Dingreville, Remi Philippe Michel, Desai, Saaketh, Walker, Elise, Shilt, Troy Patrick, Bassett, Kimberly Lundberg, Wixom, Ryan R., Stebner, Aaron, Arroyave, Raymundo, Hattrick-Simpers, Jason, Warren, James. 2023-05-03. Machine learning for materials science: Barriers to broader adoption. https://doi.org/10.1016/j.matt.2023.03.028
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