  Quantum-accurate computer simulations play a central role in understanding
phase-change materials (PCMs) for advanced memory technologies. However, direct
quantum-mechanical simulations are necessarily limited to simplified models,
containing no more than a few hundred or a thousand atoms. Machine learning
(ML) based potential models that are "trained" on quantum-mechanical data are
an emerging alternative approach, currently evolving from highly specialised to
more widely applied simulation tools. Here we show that a universal,
compositionally flexible ML model can describe a wide range of flagship
Ge-Sb-Te PCMs under real device conditions, including non-isothermal heating
and chemical disorder which are relevant for memory applications. The speed of
the ML model enables atomistic simulations of multiple thermal cycles and
delicate operations for neuro-inspired computing, namely, cumulative SET and
iterative RESET. A device-scale capability demonstration (40 x 20 x 20 nm3)
shows that the new ML potential can directly describe technologically relevant
processes in PCM-based memory products. In a wider context, our work
demonstrates how ML-driven materials simulations are now entering a stage where
they can guide architecture design for high-performance electronic devices.
