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Warning! Note that elemental potentials taken from alloy descriptions may not work well for the pure species. This is particularly true if the elements were fit for compounds instead of being optimized separately. As with all interatomic potentials, please check to make sure that the performance is adequate for your problem.
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Updated! Potentials that share interactions are now listed as related models.
2021--Sivaraman-G-Gallington-L-Krishnamoorthy-A-N-et-al--Hf-O
Citation: G. Sivaraman, L. Gallington, A.N. Krishnamoorthy, M. Stan, G. Csányi, Vázquez-Mayagoitia, and C.J. Benmore (2021), "Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide",
Physical Review Letters,
126(15), 156002. DOI:
10.1103/physrevlett.126.156002.
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, HfO2, 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.
See Computed PropertiesNotes: These files were provided by Ganesh Sivaraman on August 6, 2022. The files are also hosted at the link below.
File(s): Link(s):
Date Created: October 5, 2010 | Last updated: August 23, 2022