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

• Published in: Physical review letters Vol. 126; no. 15; p. 156002
• Main Authors: Sivaraman, Ganesh, Gallington, Leighanne, Krishnamoorthy, Anand Narayanan, Stan, Marius, Csányi, Gábor, Vázquez-Mayagoitia, Álvaro, Benmore, Chris J
• Format: Journal Article
• Language: English
• Published: United States American Physical Society 14-04-2021; American Physical Society (APS)
• Subjects: Automation; Closed loops; CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; High temperature; interatomic & molecular potentials; Machine learning; Neutron diffraction; phase diagrams; Room temperature; temperature; x-ray diffraction
• ISSN: 0031-9007; 1079-7114
• DOI: 10.1103/physrevlett.126.156002

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, HfO_{2}, 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.