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

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Mocanu, FC 
Konstantinou, Konstantinos  ORCID logo  https://orcid.org/0000-0003-1291-817X
Lee, Tae Hoon 
Bernstein, Noam 

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.

Description

Keywords

3403 Macromolecular and Materials Chemistry, 34 Chemical Sciences, Machine Learning and Artificial Intelligence, Generic health relevance

Journal Title

The Journal of Physical Chemistry Part B

Conference Name

Journal ISSN

1520-6106
1520-5207

Volume Title

122

Publisher

American Chemical Society (ACS)
Sponsorship
EPSRC (1502879)
Engineering and Physical Sciences Research Council (EP/K014560/1)
Engineering and Physical Sciences Research Council (EP/P022596/1)
Isaac Newton Trust (1624(n))
Isaac Newton Trust (17.08(c))
Leverhulme Trust (ECF-2017-278)
Engineering and Physical Sciences Research Council (EP/L015552/1)
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