2025/02/12 by Yuxing Zhou, Zhou, Yuxing, Daniel F. Thomas du Toit +7 · 2 citations
Engineering · Materials Science · #Computational Physics (physics.comp-ph) #Electronic and Structural Properties of Oxides #FOS: Physical sciences #Ferroelectric and Negative Capacitance Devices #Materials Science (cond-mat.mtrl-sci) #Semiconductor materials and devices
paper · pdf · doi:10.48550/arxiv.2502.08393
openalex publication_date 2025/02/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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. Our work provides a springboard for the atomistic modelling of PCM-based memory and neuromorphic computing devices -- and, more widely, it illustrates the power of highly efficient ACE ML models for materials science and engineering.