We present a Δ-machine learning model for obtaining Kohn–Sham accuracy from orbital-free density functional theory (DFT) calculations. In particular, we employ a machine-learned force field (MLFF) scheme based on the kernel method to capture the difference between Kohn–Sham and orbital-free DFT energies/forces. We implement this model in the context of on-the-fly molecular dynamics simulations and study its accuracy, performance, and sensitivity to parameters for representative systems. We find that the formalism not only improves the accuracy of Thomas–Fermi–von Weizsäcker orbital-free energies and forces by more than two orders of magnitude but is also more accurate than MLFFs based solely on Kohn–Sham DFT while being more efficient and less sensitive to model parameters. We apply the framework to study the structure of molten Al0.88Si0.12, the results suggesting no aggregation of Si atoms, in agreement with a previous Kohn–Sham study performed at an order of magnitude smaller length and time scales.
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28 December 2023
Research Article|
December 26 2023
Kohn–Sham accuracy from orbital-free density functional theory via Δ-machine learning Available to Purchase
Shashikant Kumar
;
Shashikant Kumar
(Conceptualization, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing)
1
College of Engineering, Georgia Institute of Technology
, Atlanta, Georgia 30332, USA
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Xin Jing
;
Xin Jing
(Methodology, Software, Writing – review & editing)
1
College of Engineering, Georgia Institute of Technology
, Atlanta, Georgia 30332, USA
2
College of Computing, Georgia Institute of Technology
, Atlanta, Georgia 30332, USA
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John E. Pask
;
John E. Pask
(Conceptualization, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing)
3
Physics Division, Lawrence Livermore National Laboratory
, Livermore, California 94550, USA
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Andrew J. Medford
;
Andrew J. Medford
(Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing)
1
College of Engineering, Georgia Institute of Technology
, Atlanta, Georgia 30332, USA
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Phanish Suryanarayana
Phanish Suryanarayana
a)
(Conceptualization, Funding acquisition, Investigation, Methodology, Software, Supervision, Writing – original draft, Writing – review & editing)
1
College of Engineering, Georgia Institute of Technology
, Atlanta, Georgia 30332, USA
2
College of Computing, Georgia Institute of Technology
, Atlanta, Georgia 30332, USA
a)Author to whom correspondence should be addressed: [email protected]
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Shashikant Kumar
1
Xin Jing
1,2
John E. Pask
3
Andrew J. Medford
1
Phanish Suryanarayana
1,2,a)
1
College of Engineering, Georgia Institute of Technology
, Atlanta, Georgia 30332, USA
2
College of Computing, Georgia Institute of Technology
, Atlanta, Georgia 30332, USA
3
Physics Division, Lawrence Livermore National Laboratory
, Livermore, California 94550, USA
a)Author to whom correspondence should be addressed: [email protected]
J. Chem. Phys. 159, 244106 (2023)
Article history
Received:
October 10 2023
Accepted:
November 30 2023
Citation
Shashikant Kumar, Xin Jing, John E. Pask, Andrew J. Medford, Phanish Suryanarayana; Kohn–Sham accuracy from orbital-free density functional theory via Δ-machine learning. J. Chem. Phys. 28 December 2023; 159 (24): 244106. https://doi.org/10.1063/5.0180541
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