CUED Publications database

An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide

Sivaraman, G and Gallington, L and Krishnamoorthy, AN and Stan, M and Csanyi, G and Vazquez-Mayagoitia, A and Benmore, CJ An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide. (Unpublished)

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Abstract

Understanding the structure and properties of refractory oxides are 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 multi-phase 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 ~2900oC. The method significantly reduces model development time and human effort.

Item Type: Article
Uncontrolled Keywords: cond-mat.mtrl-sci cond-mat.mtrl-sci cs.LG physics.comp-ph
Subjects: UNSPECIFIED
Divisions: Div C > Applied Mechanics
Depositing User: Cron Job
Date Deposited: 18 Sep 2020 20:01
Last Modified: 18 Feb 2021 15:51
DOI: