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The phase change compound Ge$_2$Sb$_2$Te$_5$ (GST225) is exploited inadvanced non-volatile electronic memories and in neuromorphic devices whichboth rely on a fast and reversible transition between the crystalline andamorphous phases induced by Joule heating. The crystallization kinetics ofGST225 is a key functional feature for the operation of these devices. Wereport here on the development of a machine-learned interatomic potential forGST225 that allowed us to perform large scale molecular dynamics simulations(over 10000 atoms for over 100 ns) to uncover the details of thecrystallization kinetics in a wide range of temperatures of interest for theprogramming of the devices. The potential is obtained by fitting with a deepneural network (NN) scheme a large quantum-mechanical database generated withinDensity Functional Theory. The availability of a highly efficient and yethighly accurate NN potential opens the possibility to simulate phase changematerials at the length and time scales of the real devices.

Unraveling the Crystallization Kinetics of the Ge$_2$Sb$_2$Te$_5$ Phase Change Compound with a Machine-Learned Interatomic Potential