BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Machine learning methods for heterogeneous catalysis - Prof Mie An
 dersen\, Aarhus University\, Denmark
DTSTART:20230213T140000Z
DTEND:20230213T143000Z
UID:TALK196663@talks.cam.ac.uk
CONTACT:Dr Christoph Schran
DESCRIPTION:Recently developed machine learning methods hold great promise
  for simultaneously reducing the computational cost and increasing the acc
 uracy in catalysis modeling\, allowing us to capture more complexity\, mak
 e our models more realistic and perhaps even obtain new physical insights 
 [1]. In my talk I will focus on recent work aimed at predicting adsorption
  configurations and energies of complex molecules at the surfaces of trans
 ition metals and alloys using a graph-based Gaussian Process Regression ML
  model (WWL-GPR [2]). We apply the methodology to study CO2 hydrogenation 
 (reverse water-gas shift) over single-atom alloy (SAA) catalysts\, i.e.\, 
 diluted bimetallic materials. SAAa have recently attracted considerable in
 terest since adsorption at the sites offered by their surfaces can break t
 he scaling relationships between adsorption energies and reaction barriers
  that limit conventional catalysts [3]. The accuracy and low cost of the a
 pplied methods allows us to consider a wide combinatorial space of element
 s of the periodic table\, paving the way toward the design and nano-engine
 ering of SAA catalysts.\n\n[1] M. Andersen and K. Reuter\, Acc. Chem. Res.
  54\, 2741 (2021).\n\n[2] W. Xu\, K. Reuter\, and M. Andersen\, Nat. Comp.
  Sci. 2\, 443 (2022).\n\n[3] RT. Hannagan et al.\, Chem. Rev. 120\, 12044 
 (2020).
LOCATION:Zoom link: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHpt
 UXlRSkppQT09
END:VEVENT
END:VCALENDAR