DAMOCLES · Design And Modeling of Oxide Catalysts by machine LEarning and atomistic Simulations

Design And Modeling of Oxide Catalysts by machine LEarning and atomistic Simulations

A major fraction of chemical reactions needed by our society is carried out on heterogeneous catalysts, solid materials that enhance the rate of desired chemical reactions without being consumed during the process. Nowadays, heterogeneous catalysts are also expected to solve the challenges related to the diversification of energy sources and the reduction of pollutants and greenhouse gases. In this view, the catalytic process of CO2 hydrogenation can pave the way to sustainably produce chemicals and fuels from waste CO2 and green hydrogen, and mitigate global warming. To make processes such as CO2 hydrogenation economically advantageous, R&D in catalysis can rely on the discovery and optimization of catalytic materials based on the combination of experimental laboratory testing, spectroscopic analysis, and theoretical studies. Density functional theory (DFT) calculations, thanks to their accurate modeling of chemical bonds, can provide fundamental insight into the reaction mechanisms and guide laboratory experiments, avoiding the alternative procedure of trial and error. However, the modeling of heterogeneous catalysis from DFT is affected by the high computational demand of the calculations and the extreme complexity of the targeted system. In the last years, machine learning (ML) surrogate models have become standard tools to deal with the high computational cost of DFT calculations. Indeed, ML models trained on limited sets of DFT-calculated data can provide fast predictions on similar systems, drastically reducing the number of calculations required. Although large efforts have been devoted to applying predictive ML modeling for the design of catalytic materials, the attention has been concentrated mainly on metallic catalysts, while only a few works have addressed metal oxides. This represents a strong limitation to this research area, as metal oxides show great performances in several catalytic reactions important for our society, including O2 evolution reaction, selective catalytic reduction of NOx, and CO2 hydrogenation. Notably, indium and zirconium oxide catalysts showed superior catalytic performances in the CO2 hydrogenation to methanol, a reaction with great potential for the power-to-fuel production chain. The overall objective of the DAMOCLES project is to advance computational catalysis by applying data-driven modeling (i.e., DFT combined with ML) to study and tailor metal oxide catalysts for CO2 hydrogenation, in search of new catalytic materials with improved performances (i.e., activity, and selectivity). By leveraging machine learning, the project seeks to model the underlying quantum mechanical principles of catalysis on oxide materials using small numbers of calculations, enabling the efficient and accurate prediction of other systems. This approach will help establish kinetic models of catalytic activity, guiding experimental efforts. In the outgoing phase of the project (first year), the postdoc Raffaele Cheula is hosted by Prof. John Kitchin at Carnegie Mellon University (USA). In the second year of the project, he returns to Aarhus University (Denmark) under the supervision of Prof. Mie Andersen.

Data: CORDIS, © European Union