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Atomic-scale machine learning for modelling memory devices - Nature Electronics
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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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