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SUMMARY:Machine learning for excited state dynamics - Rhyan Barrett\, Leip
 zig University
DTSTART:20260316T140000Z
DTEND:20260316T143000Z
UID:TALK243661@talks.cam.ac.uk
CONTACT:Isaac Parker
DESCRIPTION:Excited-state dynamics govern how photoexcited systems transfo
 rm light into chemical change\, but direct simulation remains prohibitivel
 y expensive\; even DFT can require days per picosecond of dynamics\, while
  higher-accuracy excited-state methods are rarely practical for routine st
 udies. This talk highlights how machine learning can relieve this bottlene
 ck and make excited-state dynamics accessible at scale. We present X-MACE\
 , an excited-state ML framework built on the MACE message-passing architec
 ture with an integrated diagonalization scheme implemented through an auto
 encoder\, designed to improve modelling of excited-state potential energy 
 surfaces. The emphasis is on accelerating nonadiabatic dynamics\, enabling
  surface-hopping workflows through rapid prediction of excited-state energ
 ies and related quantities\, and moving toward excited state dynamics scre
 ening. As a case study\, we outline preliminary dynamics guided design of 
 the GFP chromophore\, commonly used for bioimaging. We map key relaxation 
 pathways and explore how targeted substitutions at specific positions can 
 increase the fluoresce of the GFP chromophore. Overall\, the approach aims
  to bring excited-state dynamics into high-throughput regimes and support 
 screening for photophysical outcomes such as quantum yields in increasingl
 y complex molecular systems.
LOCATION:https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT0
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