Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential.
J Phys Chem B
; 122(38): 8998-9006, 2018 Sep 27.
Article
en En
| MEDLINE
| ID: mdl-30173522
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.
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Bases de datos:
MEDLINE
Idioma:
En
Revista:
J Phys Chem B
Asunto de la revista:
QUIMICA
Año:
2018
Tipo del documento:
Article
País de afiliación:
Reino Unido