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Citation: G. Sivaraman, L. Gallington, A.N. Krishnamoorthy, M. Stan, G. Csányi, Vázquez-Mayagoitia, and C.J. Benmore (2021), "Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide", Physical Review Letters, 126(15), 156002. DOI: 10.1103/physrevlett.126.156002.
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.

Date Created: October 5, 2010 | Last updated: August 23, 2022