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SUMMARY:Machine learning for molecular design of plasmonic nanosystems - Z
 suzsanna Koczor-Benda\, University of Warwick
DTSTART:20231106T143000Z
DTEND:20231106T150000Z
UID:TALK207985@talks.cam.ac.uk
CONTACT:Eszter Varga-Umbrich
DESCRIPTION:The interaction between molecules and strongly confined electr
 omagnetic fields at metallic\nnanostructures results in extreme enhancemen
 t of molecular spectroscopic signals. This\neffect can be utilized in new 
 nanoscale devices such as molecular terahertz (THz) detectors.\nHowever\, 
 to achieve high efficiency\, molecules with highly specialized properties 
 are\nrequired. We explore how quantum chemistry and machine learning metho
 ds can provide\ngood candidate molecules for these applications. In partic
 ular\, we investigate a promising\nnew THz detection technique based on fr
 equency upconversion by molecular vibrations\, as\ndemonstrated in Ref. [1
 ]. By screening databases containing millions of molecules\, a two-\norder
 s-of-magnitude improvement of spectral intensity can be achieved [2]. We i
 ntroduce\nan interactive online tool\, Molecular Vibration Explorer [3]\, 
 that enables further analysis of\nour quantum chemistry results for a wide
  range of surface-enhanced spectroscopic\napplications. Generative machine
  learning provides a route for going beyond existing\nmolecular databases\
 , to instead design new functional molecules by biasing towards the\ndesir
 ed properties [4]. We discuss how recent developments in conditional gener
 ative\ndesign [5] open the way for the targeted generation of functional m
 olecules.
LOCATION:Zoom link: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHpt
 UXlRSkppQT09
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