Machine learning modeling reveals unconventional nucleation mechanism in phase‐change material GeTe

Abstract

GeTe, a prototypical phase‐change material, has attracted pronounced attention for next‐generation storage and neuromorphic computing, yet its nucleation mechanism remains an ongoing pursuit. The challenge stems from the limited scales of conventional simulations and the intrinsic structural disorder of nucleation. To bridge the scale gap, we developed a machine learning potential (ML potential) trained on an extensive density functional theory dataset, enabling large‐scale molecular dynamics simulations that capture the complete crystallization pathway with quantum‐level fidelity. By employing a unified short‐ and medium‐range structural framework, we simplified the complexity and disorder inherent to nucleation, allowing us to unravel the intricate atomic environment, pinpoint essential structural motifs, and track their dynamic evolution. Through this approach, our simulations reveal an unconventional nucleation process: the initial formation of Ge‐rich clusters with defective octahedral coordination, followed by their Te‐mediated assembly into the final rock‐salt structure. This sequential mechanism presents a different scenario from classical nucleation theory's expectation of alternating Ge/Te incorporation, wherein the Te sublattice preferentially forms a face‐centered‐cubic structure ahead of Ge ordering. The observed two‐stage nucleation process adds a valuable perspective on the structural ordering kinetics in PCMs, helping to explain their fast crystallization characteristics at the atomic level.