University of Cambridge > Talks.cam > Machine learning in Physics, Chemistry and Materials discussion group (MLDG) > A machine-learned potential for the phase-change-memory material, Ge-Sb-Te

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

Add to your list(s) Download to your calendar using vCal

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.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2020 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity