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Submitted URL: https://doi.org/10.1103/PhysRevB.106.L180101
Effective URL: https://journals.aps.org/prb/abstract/10.1103/PhysRevB.106.L180101
Submission: On January 04 via automatic, source links-suspicious — Scanned from DE
Effective URL: https://journals.aps.org/prb/abstract/10.1103/PhysRevB.106.L180101
Submission: On January 04 via automatic, source links-suspicious — Scanned from DE
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MACHINE LEARNING INTERATOMIC POTENTIAL FOR SIMULATIONS OF CARBON AT EXTREME
CONDITIONS
JONATHAN T. WILLMAN, KIEN NGUYEN-CONG, ASHLEY S. WILLIAMS, ANATOLY B.
BELONOSHKO, STAN G. MOORE, AIDAN P. THOMPSON, MITCHELL A. WOOD, AND IVAN I.
OLEYNIK
PHYS. REV. B 106, L180101 – PUBLISHED 30 NOVEMBER 2022
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AbstractAuthorsArticle Text— ACKNOWLEDGMENTSSupplemental MaterialReferences
AbstractAuthorsArticle TextACKNOWLEDGMENTSSupplemental MaterialReferences
ABSTRACT
A spectral neighbor analysis (SNAP) machine learning interatomic potential
(MLIP) has been developed for simulations of carbon at extreme pressures (up to
5TPa) and temperatures (up to 20 000 K). This was achieved using a large
database of experimentally relevant quantum molecular dynamics (QMD) data,
training the SNAP potential using a robust machine learning methodology, and
performing extensive validation against QMD and experimental data. The resultant
carbon MLIP demonstrates unprecedented accuracy and transferability in
predicting the carbon phase diagram, melting curves of crystalline phases, and
the shock Hugoniot, all within 3% of QMD. By achieving quantum accuracy and
efficient implementation on leadership-class high-performance computing systems,
SNAP advances frontiers of classical MD simulations by enabling atomic-scale
insights at experimental time and length scales.
*
*
*
*
* Received 25 April 2022
* Revised 22 September 2022
* Accepted 31 October 2022
DOI:https://doi.org/10.1103/PhysRevB.106.L180101
©2022 American Physical Society
PHYSICS SUBJECT HEADINGS (PHYSH)
1. Research Areas
Chemical bondingHigh-pressure studiesPhase diagramsPhase transitions
1. Physical Systems
Carbon-based materials
1. Techniques
Machine learningMaterials modelingMolecular dynamics
Condensed Matter, Materials & Applied Physics
AUTHORS & AFFILIATIONS
Jonathan T. Willman1, Kien Nguyen-Cong1, Ashley S. Williams1, Anatoly B.
Belonoshko2, Stan G. Moore3, Aidan P. Thompson3, Mitchell A. Wood3, and Ivan I.
Oleynik1,*
* 1Department of Physics, University of South Florida, Tampa, Florida 33620,
USA
* 2Department of Physics, University of Royal Institute of Technology, 106691
Stockholm, Sweden
* 3Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
* *oleynik@usf.edu
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ISSUE
Vol. 106, Iss. 18 — 1 November 2022
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IMAGES
* FIGURE 1
SNAP training database. (a) Pressure-temperature map of QMD and static DFT
simulations included in the database, each represented by a P−T point on
carbon phase diagram sampling diamond (FC8), body-centered cubic (BC8), and
simple cubic (SC) solid and liquid phases (total number of structures - 636).
(b) Pressure-temperature-density-energy/atom distribution.
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* FIGURE 2
Validation of SNAP against QMD. (a) Carbon phase diagram, including melting
lines of diamond, BC8 and SC crystalline phases of carbon at pressures up to
5000 GPa, and hydrostatic shock Hugoniot. (b) Radial distribution functions
g(r) for diamond, BC8, and liquid phase at the diamond-BC8-liquid triple
point (840GPa, 7510K). (MD trajectories are averaged over a 20ps time
interval.) (c) Density difference Δρ=ρs−ρl between solid (s) and liquid (l)
phases (top panel) and average coordination number of carbon atoms (bottom
panel) as a function of pressure along the melting lines of diamond and BC8.
Δρ=0 corresponds to melting line maxima: diamond—at 500GPa; BC8—at 1300GPa.
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* FIGURE 3
Carbon shock Hugoniot calculated by QMD and SNAP and compared with
experimental data. (a) Us−Up Hugoniot, and (b) pressure-density Hugoniots.
Points correspond to experimental data.
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* FIGURE 4
(a) Comparison of diamond melting curves calculated by three methods: QMD
two-phase (solid line), SNAP two-phase (dashed line), and direct melting of
1-million-atom polycrystalline sample using SNAP (stars). (b) Time
progression of polycrystalline sample melting at 600GPa and 7750K. The sample
is initially composed of crystalline diamond regions (blue) separated by
grain boundaries (light blue). Liquid regions (red) emerge at the grain
boundaries, grow in size, and eventually consume the isolated diamond
crystallites.
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