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SUMMARY:Out of the Crystalline Comfort Zone: Tackling Working Interfaces w
 ith Machine Learning - Karsten Reuter\, Fritz-Haber-Institut der Max-Planc
 k-Gesellschaft
DTSTART:20250609T133000Z
DTEND:20250609T140000Z
UID:TALK231982@talks.cam.ac.uk
CONTACT:Isaac Parker
DESCRIPTION:Machine learning (ML) promises a significant enhancement of mu
 lti-scale modeling capabilities in the context of energy conversion and st
 orage (ECS). In particular\, ML interatomic potentials (MLIPs) trained wit
 h first-principles data already offer orders of magnitude speed-ups in the
  computation of predictive-quality energies and forces in atomic-scale sim
 ulations. This new efficiency finally allows to heads-on tackle the highly
  dynamic evolution of working interfaces in ECS systems\, where the target
 ed functionality like catalytic activity or ion mobility both inherently d
 rives and results from ongoing substantial structural\, compositional and 
 morphological changes. Unable to fully capture such operando evolution\, d
 irect first-principles based multiscale modeling focused hitherto on model
  (single-)crystalline surfaces or interfaces\, where the system dynamics w
 as typically restricted to select reacting or diffusing species that were 
 considered central for a targeted primary function. The MLIP-enabled enhan
 ced sampling capabilities instead allow to assess the thermodynamic stabil
 ity of complex\, possibly amorphous configurations and thereby establish r
 eliable structural models for the working interfaces. Automated process ex
 ploration in turn provides more systematic access to the elementary steps 
 that drive the operando evolution\, paving the way for microkinetic simula
 tions that analyze the entanglement of this evolution with the primary fun
 ction.
LOCATION:https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT0
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