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

G Sivaraman and L Gallington and AN Krishnamoorthy and M Stan and G Csanyi and A Vazquez-Mayagoitia and CJ Benmore, PHYSICAL REVIEW LETTERS, 126, 156002 (2021).

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, HfO2, by drawing a minimum number of training configurations from room temperature to the liquid state at similar to 2900 degrees C. The method significantly reduces model development time and human effort.

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