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Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide

Ganesh Sivaraman, Leighanne Gallington, Anand Narayanan Krishnamoorthy, Marius Stan, Gábor Csányi, Álvaro Vázquez-Mayagoitia, and Chris J. Benmore
Phys. Rev. Lett. 126, 156002 – Published 14 April 2021
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Abstract

Understanding the structure and properties of refractory oxides is critical for high temperature applications. In this work, a combined experimental and simulation approach uses an automated closed loop via an active learner, which is initialized by x-ray and neutron diffraction measurements, and sequentially improves a machine-learning model until the experimentally predetermined phase space is covered. A multiphase potential is generated for a canonical example of the archetypal refractory oxide, HfO2, by drawing a minimum number of training configurations from room temperature to the liquid state at 2900°C. The method significantly reduces model development time and human effort.

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  • Received 9 September 2020
  • Accepted 17 February 2021

DOI:https://doi.org/10.1103/PhysRevLett.126.156002

© 2021 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Ganesh Sivaraman*

  • Leadership Computing Facility, Argonne National Laboratory, Lemont, Illinois 60439, USA

Leighanne Gallington

  • X-ray Science Division, Argonne National Laboratory, Lemont, Illinois 60439, USA

Anand Narayanan Krishnamoorthy

  • Helmholtz-Institute Munster: Ionics in Energy Storage (IEK-12), Forschungszentrum Julich GmbH, Corrensstrasse 46, 48149 Munster, Germany

Marius Stan

  • Applied Materials Division, Argonne National Laboratory, Lemont, Illinois 60439, USA

Gábor Csányi

  • Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom

Álvaro Vázquez-Mayagoitia

  • Computational Science Division, Argonne National Laboratory, Lemont, Illinois 60439, USA

Chris J. Benmore

  • X-ray Science Division, Argonne National Laboratory, Lemont, Illinois 60439, USA

  • *Also at Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, USA.
  • benmore@anl.gov

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Issue

Vol. 126, Iss. 15 — 16 April 2021

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