Industrial catalysts are highly complex with events occurring across a wide range of length and time scales that are highly impacted by the operating conditions.  Unravelling this complexity is very challenging from purely experimental point of view.  Molecular modelling is instrumental to understand the functioning of an industrial catalyst but also highly challenging as one needs to bridge the length time scale gap between the molecular scale and the industrial scale.

Within this talk I will show new challenges and opportunities in modeling  complex catalytic cycles starting from the molecular scale with the ambition to understand a successful catalytic trajectory of molecule.  Such trajectory consists of events where molecules enter the catalyst,  undergo diffusion to find sites for adsorption/desorption and reaction and finally the formed products leave the catalyst.  All these events take place on vastly different time- and length scales varying from the picosecond to the second/hours and the nano- to  micrometre.  To model a full catalytic trajectory from the molecule to the crystal particle level, new techniques originating from machine learning, reaction path discovery coupled with advanced kinetic theories need to be integrated in the standard catalysis modeling workflow.  The methods will be illustrated by examples taken from C₁ catalysis to convert molecules like CO₂, CH₃OH to high-value olefins and other chemical building blocks and biomass conversion over heterogeneous catalysts.

Veronique Van Speybroeck is full professor at the Ghent University and head of the Center for Molecular modeling (http://molmod.ugent.be), a multidisciplinary research center composed of about 40 researchers.  She was trained as an engineer in Physics and obtained her PhD in 2001 from the Ghent University.  She made significant contributions to the field of modeling nanoporous materials for catalysis, adsorption, separations; all applications are inspired and performed in close synergy with experimental groups.  The research is driven by the ambition to model as close as possible realistic materials/processes. She played a pioneering role in development of molecular dynamics methods to simulate catalytic reactions at operating conditions.  Currently, she is extending the horizon to integrate machine learning methods within molecular modeling of industrial processes to resolve complex catalytic cycles bridging length and time scales.  She received two ERC grants, numerous recognitions and prizes, such as the Dr. Karl Wamsler innovation award in 2023 and the Francqui prize in exact sciences  in 2024.  She is also an elected member of the Royal (Flemish) Academy for Science and the Arts of Belgium (KVAB, www.kvab.be).