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SUMMARY:Sparse Gaussian Process Potentials and Simulations of Solid Electr
 olytes - Dr Amir Hajibabaei\, University of Cambridge
DTSTART:20221205T143000Z
DTEND:20221205T150000Z
UID:TALK193337@talks.cam.ac.uk
CONTACT:Dr Christoph Schran
DESCRIPTION:We explore sparse Gaussian process regression (SGPR) method fo
 r creation of scalable kernel-based machine learning potentials.\nIn these
  algorithms the potential energy is represented using a subset of training
  geometries called the inducing points or a set of pseudo inputs.\nParsimo
 nious sampling of the training and inducing points are the main challenges
  which are studied in the context of on-the-fly learning.\nThis methodolog
 y is demonstrated with simulations of superionic diffusion in candidate so
 lid electrolytes with emphasis on sulfides such as Li3PS4\, Li7P3S11\, etc
 .
LOCATION:Small Lecture Theatre\, Bragg Building\, Cavendish 
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