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UID:news227@dmi.unibas.ch
DTSTAMP;TZID=Europe/Zurich:20180716T202427
DTSTART;TZID=Europe/Zurich:20170512T110000
SUMMARY:Seminar in Numerical Analysis: Anatole von Lilienfeld (Universität
  Basel)
DESCRIPTION:Many of the most relevant chemical properties of matter depend 
  explicitly on atomistic and electronic details\, rendering a first  princ
 iples approach to chemistry mandatory. Alas\, even when using  high-perfor
 mance computers\, brute force high-throughput screening of  compounds is b
 eyond any capacity for all but the simplest systems and  properties due to
  the combinatorial nature of chemical space\, i.e. all  compositional\, co
 nstitutional\, and conformational isomers. Consequently\,  efficient explo
 ration algorithms need to exploit all implicit  redundancies present in ch
 emical space. I will discuss recently  developed statistical learning appr
 oaches for interpolating quantum  mechanical observables in compositional 
 and constitutional space.
X-ALT-DESC:Many of the most relevant chemical properties of matter depend  
 explicitly on atomistic and electronic details\, rendering a first  princi
 ples approach to chemistry mandatory. Alas\, even when using  high-perform
 ance computers\, brute force high-throughput screening of  compounds is be
 yond any capacity for all but the simplest systems and  properties due to 
 the combinatorial nature of chemical space\, i.e. all  compositional\, con
 stitutional\, and conformational isomers. Consequently\,  efficient explor
 ation algorithms need to exploit all implicit  redundancies present in che
 mical space. I will discuss recently  developed statistical learning appro
 aches for interpolating quantum  mechanical observables in compositional a
 nd constitutional space. 
DTEND;TZID=Europe/Zurich:20170512T120000
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