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DTSTAMP:20250822T115810Z
LOCATION:Room 5.2A17
DTSTART;TZID=Europe/Stockholm:20250616T115000
DTEND;TZID=Europe/Stockholm:20250616T122000
UID:submissions.pasc-conference.org_PASC25_sess124_msa111@linklings.com
SUMMARY:Machine Learning for Catalyst Design: Insights from Ethanol Reform
 ing and Oxygen Evolution Reaction
DESCRIPTION:Nong Artrith (Utrecht University, Debye Institute for Nanomate
 rials Science)\n\nMachine learning (ML) has emerged as a powerful tool for
  accelerating catalyst discovery by integrating computational and experime
 ntal data. While ML excels at pattern detection in large datasets, many ca
 talyst studies rely on limited experimental data. Our approach combines ML
  with first-principles calculations to extract insights from small experim
 ental datasets. We train a complex ML model on a large computational libra
 ry of transition-state energies and complement it with simple linear regre
 ssion models fitted to experimental data. This method allows us to explore
  the catalytic activity of monolayer bimetallic catalysts for ethanol refo
 rming, identifying key reactions and predicting promising compositions.\nI
 n another study, we applied ML-driven molecular dynamics and metadynamics 
 simulations to investigate the oxygen evolution reaction (OER) on pristine
  and Ni-doped BaTiO3. Using a neural network potential, we captured dynami
 c mechanistic details, revealing the impact of nickel doping on BaTiO3, a 
 perovskite oxide synthesized from earth-abundant precursors.\n\nDomain: Ch
 emistry and Materials, Physics, Computational Methods and Applied Mathemat
 ics\n\nSession Chair: Nicola Colonna (Paul Scherrer Institute)\n\n
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