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Machine learning for materials science: Barriers to broader adoption
Matter ( IF 17.3 ) Pub Date : 2023-05-03 , DOI: 10.1016/j.matt.2023.03.028
Brad Boyce , Remi Dingreville , Saaketh Desai , Elise Walker , Troy Shilt , Kimberly L. Bassett , Ryan R. Wixom , Aaron P. Stebner , Raymundo Arroyave , Jason Hattrick-Simpers , James A. Warren

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.



中文翻译:

材料科学的机器学习:更广泛采用的障碍

机器学习现在有点破旧,其进步几乎渗透到我们生活的方方面面。在材料科学领域,这股浪潮似乎正在演变成一场海啸。然而,要使其利益最大化,我们仍然面临真正的障碍。由桑迪亚国家实验室的研究人员主持并有一批名人参加的研讨会的结果,该观点简要总结了我们对这些障碍的看法。通过在社区论坛中认识到这些问题,我们可以以共同的目标和协调的努力分担解决这些问题的负担。

更新日期:2023-05-03
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