Calculation update! New properties have been added to the website for dislocation monopole core structures, dynamic relaxes of both crystal and liquid phases, and melting temperatures! Currently, the results for these properties predominately focus on EAM-style potentials, but the results will be updated for other potentials as the associated calculations finish. Feel free to give us feedback on the new properties so we can improve their representations as needed.
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.
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 Letters126(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.