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SUMMARY:Machine-learning of density functionals for applications in molecu
 les and materials
DTSTART:20180220T161500
DTEND:20180220T171500
DTSTAMP:20260407T095614Z
UID:e5a30b7a7f9fd12358cab6bc5a9f2378d5806c4d4add60de5ae3a5e8
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Kieron Burke (University of California\, Irvine)\nThis l
 ecture is designed to be accessible to a wide variety of backgrounds.\nIn 
 the first part\, I will briefly review density functional theory and why i
 t is important to many branches of modern physical science.  I will also 
 review machine learning and its recent applications to molecules and mater
 ials.\nIn the second half\,  I will show how\, in collaboration with comp
 uter scientists at TU Berlin\, we have used a specific type of machine-lea
 rning\, called kernel ridge regression\, to find more accurate and powerfu
 l approximate density functionals than any made by humans.  \nFor more ba
 ckground\, see here.\n\n14ème NCCR MARVEL "Distinguished Lecture"\nNCCR M
 ARVEL - Events\n\nSi vous désirez rencontrer le Prof. Burke\, veuillez co
 ntacter Nathalie Jongen.
LOCATION:MXF 1 https://plan.epfl.ch/?room==MXF%201
STATUS:CONFIRMED
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