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, , by drawing a minimum number of training configurations from room temperature to the liquid state at . The method significantly reduces model development time and human effort.
- Received 9 September 2020
- Accepted 17 February 2021
DOI:https://doi.org/10.1103/PhysRevLett.126.156002
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