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SUMMARY:A machine-learned potential for the phase-change-memory material\,
  Ge-Sb-Te - Prof Stephen R Elliott
DTSTART:20191111T163000Z
DTEND:20191111T170000Z
UID:TALK134533@talks.cam.ac.uk
CONTACT:Prof. Gabor Csanyi
DESCRIPTION:A machine-learned\, DFT-accurate\, linear-scaling potential (G
 AP) has been developed for the ternary material system\, Ge-Sb-Te\, in whi
 ch the composition Ge2Sb2Te5\, along the GeTe-Sb2Te3 pseudo-binary tie-lin
 e\, is the canonical material for next-generation phase-change random-acce
 ss memory (PCRAM) applications. The potential has been used to generate gl
 assy models as large as 24\,300 atoms\, and ensembles of smaller glassy mo
 dels\, prepared under identical conditions\, for a statistical study of th
 e electronic properties\, calculated using DFT methods\, in particular the
  prevalence and nature of ‘defect’ states with energies lying in the b
 andgap of this semiconducting material.\n\n[1] F Mocanu et al\, J. Phys. C
 hem. 122\, 8998 (2018)\n[2] K Konstantinou et al\, Nat. Comm. 10\, 3065 (2
 019)
LOCATION:Mott Seminar (531) room\, top floor of the Mott Building\, in the
  Cavendish Laboratory\, West Cambridge.
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