A machine-learned potential for the phase-change-memory material, Ge-Sb-Te

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• Prof Stephen R Elliott
• Monday 11 November 2019, 16:30-17:00
• Mott Seminar (531) room, top floor of the Mott Building, in the Cavendish Laboratory, West Cambridge..

If you have a question about this talk, please contact Prof. Gabor Csanyi.

A machine-learned, DFT -accurate, linear-scaling potential (GAP) has been developed for the ternary material system, Ge-Sb-Te, in which the composition Ge2Sb2Te5, along the GeTe-Sb2Te3 pseudo-binary tie-line, is the canonical material for next-generation phase-change random-access memory (PCRAM) applications. The potential has been used to generate glassy models as large as 24,300 atoms, and ensembles of smaller glassy models, prepared under identical conditions, for a statistical study of the electronic properties, calculated using DFT methods, in particular the prevalence and nature of ‘defect’ states with energies lying in the bandgap of this semiconducting material.

[1] F Mocanu et al, J. Phys. Chem. 122, 8998 (2018) [2] K Konstantinou et al, Nat. Comm. 10, 3065 (2019)

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

This talk is included in these lists:

• Hanchen DaDaDash
• Lennard-Jones Centre external
• Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
• Mott Seminar (531) room, top floor of the Mott Building, in the Cavendish Laboratory, West Cambridge.

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