CUED Publications database

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

Mocanu, FC and Konstantinou, K and Lee, TH and Bernstein, N and Deringer, V and Csanyi, G and Elliott, S (2018) Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential. The Journal of Physical Chemistry Part B, 122. pp. 8998-9006. ISSN 1520-6106 (Unpublished)

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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 physico-chemical 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 7,200-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 non-volatile memory devices.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div C > Applied Mechanics
Div C > Materials Engineering
Depositing User: Cron Job
Date Deposited: 12 Sep 2018 20:22
Last Modified: 02 Mar 2021 06:54
DOI: 10.1021/acs.jpcb.8b06476