More is Different: Modern Computational Modeling for Heterogeneous Catalysis

The combination of Experimental observations and Density Functional Theory studies is one of the pillars of modern chemical research. As they enable the collection of additional physical information of a chemical system, hardly accessible via the experimental setting, Density Functional Theory studies are widely employed to model and predict the behavior of a diverse variety of chemical compounds under unique environments. Particularly, in heterogeneous catalysis, Density Functional Theory models are commonly employed to evaluate the interaction between molecular compounds and catalysts, lately linking these interpretations with experimental results. However, high complexity found in both, catalytic settings and reactivity, implies the need of sophisticated methodologies involving automation, storage and analysis to correctly study these systems. Here, I present the development and combination of multiple methodologies, aiming at correctly asses complexity. Also, this work shows how the provided techniques have been actively used to study novel catalytic settings of academic and industrial interest.

This thesis presents a set of tools to ease the computational study of catalytic systems, as well as their combination to solve high complexity problems. I successfully tested these tools on different production environments to evaluate their usability and accuracy, being able to automate DFT calculations, identify chemical descriptors, make activity predictions and explore complex neural network. This works paves the way in the use of computational tools to deal with the high complexity growth found in heterogeneous catalysis.

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