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SUMMARY:Statistical learning for phase-change memory materials - Felix Cos
 min Mocanu
DTSTART:20200817T160000Z
DTEND:20200817T163000Z
UID:TALK150310@talks.cam.ac.uk
CONTACT:Bingqing Cheng
DESCRIPTION:The marriage of accurate quantum mechanical simulations and ex
 pressive descriptors of local atomic environments has proven extremely fru
 itful for the predictive modelling of materials. We present several exampl
 es which leverage accurate quantum mechanical simulations and statistical 
 learning techniques for the investigation of phase-change memory materials
  in the ternary Ge-Sb-Te system. The talk will go over: (i) the physics\, 
 chemistry and engineering of phase-change memory materials\, and their pot
 ential uses in beyond-silicon hardware for AI applications\, (ii) the use 
 of atomic descriptors for visualising atomic local and global structural s
 imilarity in disordered phase-change memory materials\, (iii) the training
  of an approximate interatomic potential for the canonical Ge2Sb2Te5 compo
 sition and the Ge-Sb-Te system more generally\, from quantum mechanical da
 ta (density-functional theory calculations) using the sparse and regularis
 ed gaussian approximation potential (GAP) framework [1]. We end by highlig
 hting some of the modelling done with the Ge-Sb-Te GAP potential\, the lim
 itations in terms of accuracy and transferability and the scope for future
  improvement and exploitation of the models.\n\n[1] Felix C. Mocanu et al.
  “Modeling the Phase-Change Memory Material\, Ge2Sb2Te5\, with a Machine
 -Learned Interatomic Potential”. J. Phys. Chem. B 122.38 (Sept. 2018). p
 p. 8998–9006. doi: 10.1021/acs.jpcb.8b06476.
LOCATION:virtual ZOOM meeting ID: 263 591 6003\, https://zoom.us/j/2635916
 003
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