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Full-cycle device-scale simulations of memory materials with a tailored atomic-cluster-expansion potential

Author

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  • Yuxing Zhou

    (University of Oxford)

  • Daniel F. Thomas du Toit

    (University of Oxford)

  • Stephen R. Elliott

    (University of Oxford)

  • Wei Zhang

    (Xi’an Jiaotong University)

  • Volker L. Deringer

    (University of Oxford)

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.

Suggested Citation

  • Yuxing Zhou & Daniel F. Thomas du Toit & Stephen R. Elliott & Wei Zhang & Volker L. Deringer, 2025. "Full-cycle device-scale simulations of memory materials with a tailored atomic-cluster-expansion potential," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63732-4
    DOI: 10.1038/s41467-025-63732-4
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