Machine learning for materials science: Barriers to broader adoption

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

doi:10.1016/j.matt.2023.03.028

Machine learning for materials science: Barriers to broader adoption

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

Research output: Contribution to journal › Comment/debate

25 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	1320-1323
Number of pages	4
Journal	Matter
Volume	6
Issue number	5
DOIs	https://doi.org/10.1016/j.matt.2023.03.028
State	Published - May 3 2023
Externally published	Yes

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10.1016/j.matt.2023.03.028

Cite this

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title = "Machine learning for materials science: Barriers to broader adoption",

abstract = "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.",

author = "Brad Boyce and Remi Dingreville and Saaketh Desai and Elise Walker and Troy Shilt and Bassett, \{Kimberly L.\} and Wixom, \{Ryan R.\} and Stebner, \{Aaron P.\} and Raymundo Arroyave and Jason Hattrick-Simpers and Warren, \{James A.\}",

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year = "2023",

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doi = "10.1016/j.matt.2023.03.028",

language = "English",

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TY - JOUR

T1 - Machine learning for materials science

T2 - Barriers to broader adoption

AU - Boyce, Brad

AU - Dingreville, Remi

AU - Desai, Saaketh

AU - Walker, Elise

AU - Shilt, Troy

AU - Bassett, Kimberly L.

AU - Wixom, Ryan R.

AU - Stebner, Aaron P.

AU - Arroyave, Raymundo

AU - Hattrick-Simpers, Jason

AU - Warren, James A.

PY - 2023/5/3

Y1 - 2023/5/3

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85153487573

U2 - 10.1016/j.matt.2023.03.028

DO - 10.1016/j.matt.2023.03.028

M3 - Comment/debate

AN - SCOPUS:85153487573

SN - 2590-2393

VL - 6

SP - 1320

EP - 1323

JO - Matter

JF - Matter

IS - 5

ER -

Machine learning for materials science: Barriers to broader adoption

Abstract

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