BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Quantum Machine Learning - Professor Anatole von Lilienfeld\, Univ
 ersity of Basel
DTSTART:20171108T141500Z
DTEND:20171108T151500Z
UID:TALK74971@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION: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-performa
 nce computers\, brute force high-throughput screening of compounds is beyo
 nd any capacity for all but the simplest systems and properties due to the
  combinatorial nature of chemical space\, i.e. all compositional\, constit
 utional\, and conformational isomers. Consequently\, efficient exploration
  algorithms need to exploit all implicit redundancies present in chemical 
 space. I will discuss recently developed statistical learning approaches f
 or interpolating quantum mechanical observables in compositional and const
 itutional space.
LOCATION:Department of Chemistry\, Cambridge\, Unilever lecture theatre
END:VEVENT
END:VCALENDAR