TY - JOUR
T1 - Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential
AU - Mocanu, Felix C.
AU - Konstantinou, Konstantinos
AU - Lee, Tae Hoon
AU - Bernstein, Noam
AU - Deringer, Volker L.
AU - Csányi, Gábor
AU - Elliott, Stephen R.
N1 - Publisher Copyright:
© 2018 American Chemical Society.
PY - 2018/9/27
Y1 - 2018/9/27
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85053627941&partnerID=8YFLogxK
U2 - 10.1021/acs.jpcb.8b06476
DO - 10.1021/acs.jpcb.8b06476
M3 - Article
C2 - 30173522
AN - SCOPUS:85053627941
SN - 1520-6106
VL - 122
SP - 8998
EP - 9006
JO - Journal of Physical Chemistry B
JF - Journal of Physical Chemistry B
IS - 38
ER -