Partial melting nature of phase-change memory Ge-Sb-Te superlattice uncovered by large-scale machine learning interatomic potential molecular dynamics

[GeTe] _m-[Sb ₂Te ₃] _n superlattice (GST-SL) is a promising material candidate to solve the critical problem of high-power consumption for phase-change memory technology. However, the switching mechanism is still under strong debate during the last decade. A key controversial question is that whether melting in GST-SL is possible. In this work, a large-scale machine learning interatomic potential (MLIP) molecular dynamics (MD) simulation with a one-order-of-magnitude larger atom number than that of conventional density functional theory (DFT) MD captures a unique partial melting behavior in GST-SL, where a sparse-nucleation-and-growth governed melting behavior is triggered by the flipping of atoms into van der Waals (vdW) gaps and forming cation-in-vdW-gap (CiV) defect as starting point, and then is slowly developed via propagating liquid-crystalline interfaces. In great contrast, the traditional melting of rock salt (RS)-GST occurs very fast due to instant homogeneous nucleation from high-density intrinsic vacancies distributed randomly in its cubic lattice. Therefore, the melting regions in GST-SL can be spatially localized in form of partial melting and are readily controlled. Moreover, the atomic distribution after melting in GST-SL is more chemical ordering than that in RS-GST. By the large-scale MLIP-MD, the present study provides a critical atomic insight into GST-SL phase-change behaviors that conventional DFT-MDs hardly achieve. This partial-melting nature in GST-SL helps explain the long-term-confused working principle of GST-SL-based phase-change memory, which will accelerate its application in the big-data era.