Computational Data-Driven Materials Discovery

Arun Mannodi-Kanakkithodi; Maria K.Y. Chan

doi:10.1016/j.trechm.2020.12.007

Computational Data-Driven Materials Discovery

Arun Mannodi-Kanakkithodi^*, Maria K.Y. Chan

^*Corresponding author for this work

MRC - Materials Research Center

Research output: Contribution to journal › Short survey › peer-review

21 Scopus citations

Abstract

Machine learning (ML) from large materials datasets enables accelerated materials discovery. Currently, the most accessible way to generate uniform, well-curated, voluminous datasets is by the application of high-throughput first principles computations. Here, we present the guiding principles of using computational data and ML to drive new materials discovery.

Original language	English (US)
Pages (from-to)	79-82
Number of pages	4
Journal	Trends in Chemistry
Volume	3
Issue number	2
DOIs	https://doi.org/10.1016/j.trechm.2020.12.007
State	Published - Feb 2021

Funding

This work was performed, in part, at the Center for Nanoscale Materials, a US Department of Energy Office of Science User Facility supported by the US Department of Energy, Office of Science, under contract number DE-AC02-06CH11357. We acknowledge funding from the US Department of Energy SunShot program under contract number DOE DEEE005956. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy under contract number DE-AC02-05CH11231. We gratefully acknowledge the computing resources provided on Bebop, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory. This work was performed, in part, at the Center for Nanoscale Materials, a US Department of Energy Office of Science User Facility supported by the US Department of Energy , Office of Science, under contract number DE-AC02-06CH11357 . We acknowledge funding from the US Department of Energy SunShot program under contract number DOE DEEE005956 . This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy under contract number DE-AC02-05CH11231 . We gratefully acknowledge the computing resources provided on Bebop, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory.

Keywords

computational materials design
first principles density functional theory
machine learning

ASJC Scopus subject areas

General Chemistry

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10.1016/j.trechm.2020.12.007

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title = "Computational Data-Driven Materials Discovery",

abstract = "Machine learning (ML) from large materials datasets enables accelerated materials discovery. Currently, the most accessible way to generate uniform, well-curated, voluminous datasets is by the application of high-throughput first principles computations. Here, we present the guiding principles of using computational data and ML to drive new materials discovery.",

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author = "Arun Mannodi-Kanakkithodi and Chan, {Maria K.Y.}",

note = "Funding Information: This work was performed, in part, at the Center for Nanoscale Materials, a US Department of Energy Office of Science User Facility supported by the US Department of Energy, Office of Science, under contract number DE-AC02-06CH11357. We acknowledge funding from the US Department of Energy SunShot program under contract number DOE DEEE005956. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy under contract number DE-AC02-05CH11231. We gratefully acknowledge the computing resources provided on Bebop, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory. Funding Information: This work was performed, in part, at the Center for Nanoscale Materials, a US Department of Energy Office of Science User Facility supported by the US Department of Energy , Office of Science, under contract number DE-AC02-06CH11357 . We acknowledge funding from the US Department of Energy SunShot program under contract number DOE DEEE005956 . This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy under contract number DE-AC02-05CH11231 . We gratefully acknowledge the computing resources provided on Bebop, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory. Publisher Copyright: {\textcopyright} 2020 Elsevier Inc.",

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doi = "10.1016/j.trechm.2020.12.007",

language = "English (US)",

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pages = "79--82",

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AU - Mannodi-Kanakkithodi, Arun

AU - Chan, Maria K.Y.

N1 - Funding Information: This work was performed, in part, at the Center for Nanoscale Materials, a US Department of Energy Office of Science User Facility supported by the US Department of Energy, Office of Science, under contract number DE-AC02-06CH11357. We acknowledge funding from the US Department of Energy SunShot program under contract number DOE DEEE005956. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy under contract number DE-AC02-05CH11231. We gratefully acknowledge the computing resources provided on Bebop, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory. Funding Information: This work was performed, in part, at the Center for Nanoscale Materials, a US Department of Energy Office of Science User Facility supported by the US Department of Energy , Office of Science, under contract number DE-AC02-06CH11357 . We acknowledge funding from the US Department of Energy SunShot program under contract number DOE DEEE005956 . This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy under contract number DE-AC02-05CH11231 . We gratefully acknowledge the computing resources provided on Bebop, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory. Publisher Copyright: © 2020 Elsevier Inc.

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N2 - Machine learning (ML) from large materials datasets enables accelerated materials discovery. Currently, the most accessible way to generate uniform, well-curated, voluminous datasets is by the application of high-throughput first principles computations. Here, we present the guiding principles of using computational data and ML to drive new materials discovery.

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Computational Data-Driven Materials Discovery

Abstract

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Keywords

ASJC Scopus subject areas

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