Catalysts are everywhere. They help accelerate and enable essential industrial processes towards success by being selective towards certain products and producing higher yields of said products at an agile manner. Nowadays, the relevance of catalysts is not only in industrial production, but also in the development of novel structures which are able to provide reaction pathways of relevant processes, such as the production of green hydrogen, or conversion of $CO_2$ into relevant products. For this, computational simulations are used as the first step in screening potential candidates that are able to provide higher yields or selectivity in heterogeneous catalysis reactions. However, these simulations are mainly done through the use of DFT, which requires a high computational cost and convergence time. Machine Learned Interatomic Potentials (MLIPs) have risen as complements for DFT simulations via training and learning from DFT energies and forces data to provide a platform for molecular dynamics simulations used to study the movement and behavior of atoms and molecules over time. In this research project, an active learning loop is engineered with the purpose of automating the workflow of training, using, and fine-tuning a MLIP (in this case, MACE) for its further use in catalysis energetics calculations.

The energy mix of the future is likely to feature hydrogen due to its versatility. For effective use in energy storage, hydrogen has to be compressed. Conventional electrolysis of water and subsequent mechanical compression of hydrogen is an energy intensive process. A one-step conversion and compression can take place in a Electrochemical Hydrogen Compressor (EHC). This has the disadvantage that the resulting stream of high pressure hydrogen contains water. In order to assess the capability of hydrophilic zeolites as a means of selectively adsorbing this water, a in silico screening study of 6 zeolites was performed. To this end, a force field was constructed to allow for the simulation of high pressure hydrogen dehydration using Monte Carlo methods. The validity of this force field was evaluated by replicating simulation studies of adsorption of water/ hydrogen on zeolite frameworks with the presence of extra-framework cations. At the system pressure of 875 bar, prediction of the fugacity coefficient by means of the Peng-Robinson Equation of State (PR-EOS) yields inaccurate results. Therefore, these are calculated in the CFCNPT ensemble. It is demonstrated that the Ideal Adsorbed Solution Theory (IAST) is not suited to predict binary adsorption isotherms in this system. As a result of the screening study, it is found that the selectivity of water over hydrogen is almost linearly correlated to the amount of Al atoms in the zeolite framework. Furthermore, it is observed that topologies which feature a low amount of void space outperform those where significant void spaces are present. This can be attributed to the fact that the interactions of the zeolite with water are stronger than those with hydrogen in the limit of a low Si/Al ratio (Si/Al=1). It is theorized that water adsorbs preferentially at the surface of the zeolite, and competitive adsorption by hydrogen can only take place in sufficiently large void spaces.