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SUMMARY:Data-driven material discovery and synthesis for perovskite optoel
 ectronic materials - Yilei Wu\, University of Cambridge
DTSTART:20250512T133000Z
DTEND:20250512T140000Z
UID:TALK231958@talks.cam.ac.uk
CONTACT:Kang Wang
DESCRIPTION:Perovskites have attracted widespread attention in various app
 lications such as optoelectronic devices\, catalysis\, and flexible electr
 onic devices due to their excellent optoelectronic properties and cost-eff
 ective fabrication. Despite promising prospects\, obstacles in their comme
 rcial applications necessitate addressing the shortcomings of existing per
 ovskites on one hand and discovering novel and high-performance perovskite
 s on the other hand. With the significant progress in theoretical methods 
 and computational ability\, various material simulation methods and machin
 e learning (ML) techniques can be utilized to efficiently design novel per
 ovskite materials and guide the experimental synthesis. In this report\, I
  will describe my recent works on data-driven material discovery and synth
 esis for perovskite optoelectronic materials. In the first\, I will introd
 uce a ML-aided interpretable approach for photovoltaic properties of two-d
 imensional/three-dimensional (2D/3D) perovskite heterojunctions. Physicoch
 emical insights\, ML techniques\, and causal inference are integrated to e
 xplore the key factors influencing formation\, stability\, and photovoltai
 c performance of 2D/3D perovskite heterojunctions. In the second\, I will 
 introduce the ML-accelerated experimental synthesis of 2D silver/bismuth p
 erovskites. Compared to the traditional approach based on chemical intuiti
 on\, our strategy have increased the success rate of the synthesis feasibi
 lity by a factor of four.
LOCATION:https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT0
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