Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential

Felix C. Mocanu, Konstantinos Konstantinou, Tae Hoon Lee, Noam Bernstein, Volker L. Deringer, Gábor Csányi, Stephen R. Elliott

Research output: Contribution to journalArticlepeer-review

125 Scopus citations

Abstract

The phase-change material, Ge2Sb2Te5, is the canonical material ingredient for next-generation storage-class memory devices used in novel computing architectures, but fundamental questions remain regarding its atomic structure and physicochemical properties. Here, we introduce a machine-learning (ML)-based interatomic potential that enables large-scale atomistic simulations of liquid, amorphous, and crystalline Ge2Sb2Te5 with an unprecedented combination of speed and density functional theory (DFT) level of accuracy. Two applications exemplify the usefulness of such an ML-driven approach: we generate a 7200-atom structural model, hitherto inaccessible with DFT simulations, that affords new insight into the medium-range structural order and we create an ensemble of uncorrelated, smaller structures, for studies of their chemical bonding with statistical significance. Our work opens the way for new atomistic insights into the fascinating and chemically complex class of phase-change materials that are used in real nonvolatile memory devices.

Original languageEnglish
Pages (from-to)8998-9006
Number of pages9
JournalJournal of Physical Chemistry B
Volume122
Issue number38
DOIs
StatePublished - 27 Sep 2018

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