Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide
Ganesh Sivaraman, Leighanne C. Gallington, Anand Narayanan Krishnamoorthy, Marius Stan, Gábor Cśanyi, Álvaro Vázquez‐Mayagoitia, Chris J. Benmore
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, 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.