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SUMMARY:"Machine learning in chemistry and beyond" (ChE-605) seminar by Pr
 of. Klavs F. Jensen: "Accelerating Chemical Discovery and Development with
  Machine Learning\, Robotics\, and Automation"
DTSTART:20251118T171500
DTEND:20251118T181500
DTSTAMP:20260921T211552Z
UID:d8b6f0bcada7ac84cf988b85a7408abe2bfb1d8ad2fb4fc5eba3a6ea
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Klavs Flemming Jensen is a chemical engineer who is curr
 ently the Warren K. Lewis Professor at the Massachusetts Institute of Tech
 nology (MIT). Prof. Jensen was elected a member of the National Academy of
  Engineering in 2002 for fundamental contributions to multi-scale chemical
  reaction engineering with important applications in microelectronic mater
 ials processing and microreactor technology. From 2007 to July 2015 he was
  the Head of the Department of Chemical Engineering at MIT.\nMachine learn
 ing (ML) tools are becoming increasingly effective at generating new candi
 date molecules\, predicting their properties\, proposing reaction pathways
  through computer-aided synthesis planning (CASP)\, and analyzing analytic
 al data. Automation and robotic technologies have also become easier to us
 e and more affordable to implement\, enabling automated chemical synthesis
  and characterization with little or no human intervention once the system
  is set up. Case studies demonstrate how automated synthesis systems integ
 rated with ML algorithms create autonomous chemical discovery platforms ca
 pable of operating across diverse chemical spaces with minimal manual effo
 rt\, improving the traditional design-make-test-analyze (DMTA) workflow. T
 he synthesis of new organic dye molecules exemplifies that property–focu
 sed discovery platforms can suggest and synthesize molecules to expand tra
 ining datasets for ML generative and property-prediction models\, helping 
 to map the chemical space and ultimately identify top-performing molecules
 . A second example illustrates how the automated platform can be easily ad
 apted to discover histone deacetylase inhibitors by modifying the underlyi
 ng ML models and using Bayesian optimization to balance experimental costs
  with the number of molecules screened during sequential rounds of virtual
 \, coarse\, and refined experimentation. A final example\, identifying ele
 ctrochemical oxidation transformations and their optimal reaction conditio
 ns\, highlights how Large Language Models (LLMs) further facilitate the in
 tegration of AI tools into automated chemical experimentation. Challenges\
 , opportunities\, and the role of the human operator are discussed in all 
 the case studies.
LOCATION:BCH 2218 https://plan.epfl.ch/?room==BCH%202218 https://epfl.zoom
 .us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYINRdz09
STATUS:CONFIRMED
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