Designing

hexane computer chip

Heterogeneous catalysts are highly complex systems, which are difficult to characterize and for which obtaining molecular-level structure-activity relationships is rare and typically challenging to obtain, hence these materials are often still developed mostly through empirical approaches (trials and errors or using via indirect correlations).
Thanks to the use of well-defined catalysts, prepared via SOMC, our group is able to employ state-of-the-art, modelling and data-chemometric approaches to tackle this problem.
We thus use computational methods to probe reaction mechanism (e.g. evaluating potential energy surfaces) as well as to model the spectroscopic signatures of surface species and reaction intermediates, building relations between spectroscopic signature, electronic structure and reactivity. With more complex systems, such as supported nanoparticles for which the number of configurations increases to almost infinity and where the chemical state can be dynamic, depending on reaction conditions (change of chemical potentials), we also explore methods such as ab initio metadynamics simulation to understand the dynamic behavior of materials and the spectroscopic response.
Finally, our research effort also encompasses data-analysis to identify possible hidden correlations through the use of multivariate analysis for instance. We also use data-driven catalyst discovery and exploration in our HTE approaches with the ultimate goal to not only identify better catalysts but moreover guideline principles for catalyst development (see below).
 

Computational Design

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