A systematic approach to generating accurate neural network potentials: the case of carbon
Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling. Artificial neural network-based approaches for generating potentials are promising; however, neural network training requires large amounts of data, sampled ad...
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Published in | npj computational materials Vol. 7; no. 1; pp. 1 - 13 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
14.04.2021
Nature Publishing Group Nature Portfolio |
Subjects | |
Online Access | Get full text |
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Summary: | Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling. Artificial neural network-based approaches for generating potentials are promising; however, neural network training requires large amounts of data, sampled adequately from an often unknown potential energy surface. Here we propose a self-consistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis, to construct an accurate, inexpensive, and transferable artificial neural network potential. Using this approach, we construct an interatomic potential for carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond, graphite, and graphene, as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases. |
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Bibliography: | USDOE |
ISSN: | 2057-3960 2057-3960 |
DOI: | 10.1038/s41524-021-00508-6 |