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: Y. Zhou, D.F. Thomas du Toit, S.R. Elliott, W. Zhang, and V.L. Deringer (2025), "Full-cycle device-scale simulations of memory materials with a tailored atomic-cluster-expansion potential", Nature Communications16(1). DOI: 10.1038/s41467-025-63732-4.
Abstract: Computer simulations have long been key to understanding and designing phase-change materials (PCMs) for memory technologies. Machine learning is now increasingly being used to accelerate the modelling of PCMs, and yet it remains challenging to simultaneously reach the length and time scales required to simulate the operation of real-world PCM devices. Here, we show how ultra-fast machine-learned interatomic potentials, based on the atomic cluster expansion (ACE) framework, enable simulations of PCMs reflecting applications in devices with excellent scalability on high-performance computing platforms. We report full-cycle simulations-including the time-consuming crystallisation process (from digital "zeroes" to "ones")-thus representing the entire programming cycle for cross-point memory devices. We also showcase a simulation of full-cycle operations, relevant to neuromorphic computing, in a mushroom-type device geometry.
See Computed Properties Notes: This file was provided by Sergei Starikov on July 15, 2026. The link was pulled from the paper and contains training and testing data. File(s):