Veronique Van Speybroeck

When a feed of molecules is sent over a nanoporous solid, they undergo a fascinating journey characterized by various events like adsorption, diffusion, reaction (Figure 1). Eventually after having spent some time in the material, they escape from the lattice either in transformed or changed form. All the sketched events, are characterized by vastly different length and time scales varying from the picosecond to the second/hours and the nano- to the micrometre. Length and time phenomena are deeply intertwined, any change made on the nanometre scale impacts the further time behaviour of the molecules. Furthermore the behavior of an active catalyst is strongly affected by the operating conditions, catalysts also dynamically evolve in time. Modeling such catalysts in action from the molecular level is extremely challenging. In today’s modeling practice, each of the sketched events is modeled in an isolated way with inconsistent methods, i.e. reactions are modeled with quantum mechanical methods whereas transport phenomena are modeled with classical force fields. In this talk, evidence is given that reaction, diffusion and adsorption are dynamically entangled under realistic time-dependent operating conditions and that we need quantum-accurate dynamics across scales to simulate the true behavior of a catalyst. In this context, machine‑learning potentials (MLPs)—numerical potentials trained on quantum‑mechanical data—are instrumental for extending simulations to much longer length and time scales. Key reactions and intermediates occurring during the CO2 to hydrocarbons and methanol conversion over zeolites are discussed. The results show how MLPs provide quantum‑accurate dynamic insights into individual catalytic steps. However to obtain an overall time perspective of a molecular trajectory, connection with other multiscale kinetic and transport models will be necessary, ultimately enabling a direct connection between detailed molecular events and the full time evolution of industrial catalysts under realistic conditions.