Machine learning the metastable phase diagram of covalently bonded carbon

Srilok Srinivasan; Rohit Batra; Duan Luo; Troy Loeffler; Sukriti Manna; Henry Chan; Liuxiang Yang; Wenge Yang; Jianguo Wen; Pierre Darancet; Subramanian K.R.S. Sankaranarayanan

doi:10.1038/s41467-022-30820-8

Nature Communications (Jun 2022)

Machine learning the metastable phase diagram of covalently bonded carbon

Srilok Srinivasan,
Rohit Batra,
Duan Luo,
Troy Loeffler,
Sukriti Manna,
Henry Chan,
Liuxiang Yang,
Wenge Yang,
Jianguo Wen,
Pierre Darancet,
Subramanian K.R.S. Sankaranarayanan

Affiliations

Srilok Srinivasan: Center for Nanoscale Materials, Argonne National Laboratory
Rohit Batra: Center for Nanoscale Materials, Argonne National Laboratory
Duan Luo: Center for Nanoscale Materials, Argonne National Laboratory
Troy Loeffler: Center for Nanoscale Materials, Argonne National Laboratory
Sukriti Manna: Center for Nanoscale Materials, Argonne National Laboratory
Henry Chan: Center for Nanoscale Materials, Argonne National Laboratory
Liuxiang Yang: Center for High Pressure Science and Technology Advanced Research
Wenge Yang: Center for High Pressure Science and Technology Advanced Research
Jianguo Wen: Center for Nanoscale Materials, Argonne National Laboratory
Pierre Darancet: Center for Nanoscale Materials, Argonne National Laboratory
Subramanian K.R.S. Sankaranarayanan: Center for Nanoscale Materials, Argonne National Laboratory

DOI: https://doi.org/10.1038/s41467-022-30820-8
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 12

Abstract

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Exploration of metastable phases of a given elemental composition is a data-intensive task. Here the authors integrate first-principles atomistic simulations with machine learning and high-performance computing to allow a rapid exploration of the metastable phases of carbon.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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