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An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide
- Source :
- Phys. Rev. Lett. 126, 156002 (2021)
- Publication Year :
- 2020
-
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.
Details
- Database :
- arXiv
- Journal :
- Phys. Rev. Lett. 126, 156002 (2021)
- Publication Type :
- Report
- Accession number :
- edsarx.2009.04045
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1103/PhysRevLett.126.156002