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SUMMARY:Remembering the Lab in Computational Molecular Material Discovery 
 - Professor Kim Jelfs\, Imperial College London
DTSTART:20251105T143000Z
DTEND:20251105T153000Z
UID:TALK231529@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:We have been developing computational software towards assisti
 ng in the discovery of molecular materials with targeted structures and pr
 operties. While initially we have focused upon porous molecular materials\
 , we will also address the ways in which our approach is generalisable to 
 other molecular materials and their applications\, including as organic se
 miconductors or for photocatalysis. Our evolutionary algorithm automates t
 he assembly of hypothetical molecules from a library of precursors. Our ap
 proach has already suggested promising targets that have been syntheticall
 y realised. We have also examined the application of both supervised machi
 ne learning and explainable graph neural networks for the rapid prediction
  of porous molecules’ properties. Finally\, we have trained a model (the
  Materials Precursor Score\, MPScore) to guide our predictions to select m
 aterials that have a high chance of being synthesisable in the laboratory.
  We will also discuss our experimental work to gather data for improved mo
 dels. Our EPSRC AI hub for Chemistry’s (AIchemy) goals and research will
  also be discussed.
LOCATION:Unilever Lecture Theatre\, Yusuf Hamied Department of Chemistry
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