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SUMMARY:Machine learning for nanoporous materials design - Prof. Mohamad M
 oosavi\, Chemical Engineering\, The University of Toronto
DTSTART:20230213T143000Z
DTEND:20230213T150000Z
UID:TALK196774@talks.cam.ac.uk
CONTACT:Dr Venkat Kapil
DESCRIPTION:The success of the research on metal-organic frameworks (MOFs)
  and related porous materials over the past two decades makes it now possi
 ble to believe we can tailor-make materials with desired properties for se
 veral key environmental-related applications\, such as carbon capture and 
 energy storage. To fully realize the potential of this development\, we ne
 ed to find materials that perform optimally in multi-scale processes\, con
 sidering scales from the molecular level to the chemical plant. However\, 
 this requires a holistic perspective over the full design and discovery pr
 ocess\, which involves exploring immense materials spaces\, various materi
 al properties\, their synthesis\, as well as process design and engineerin
 g. The complexity of exploring all potential options by conventional scien
 tific approaches seems intractable. Therefore\, we are now developing tool
 s from the field of machine learning and artificial intelligence that will
  enable us to pursue our aim of understanding and designing materials in a
  new way. \n\nIn my talk\, I will discuss some steps toward this approach\
 , presenting machine learning methods in the context of the rational desig
 n of MOFs for adsorption applications\, starting from the molecular scale 
 with a view toward the next steps. We will discuss why and how to quantify
  the structural diversity of MOF material databases\, assess newly-reporte
 d materials' novelty\, design MOFs with desired thermal properties\, autom
 ate force field generation\, and learn from failed experiments.
LOCATION:Zoom link: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHpt
 UXlRSkppQT09
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