Machine-learning-driven atomistic simulations are shown to describe phase-change materials on the length scale of real devices. This demonstration suggests that the atomic-scale design of phase-change architectures, programming conditions and full devices could be within reach.
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References
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This is a summary of: Zhou, Y. et al. Device-scale atomistic modelling of phase-change memory materials. Nat. Electron. https://doi.org/10.1038/s41928-023-01030-x (2023).
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Atomic-scale machine learning for modelling memory devices. Nat Electron 6, 726–727 (2023). https://doi.org/10.1038/s41928-023-01031-w
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DOI: https://doi.org/10.1038/s41928-023-01031-w
