Themed collection Digital Catalysis

Computational design of catalytic materials is a high dimensional structure optimization problem that is limited by the bottleneck of expensive quantum computation tools. An illustration of interaction of different factors involved in the design and optimization of a catalyst.

We computationally identified Ni₃Fe as a promising catalyst for the quinoline (QL) selective hydrogenation to 1,2,3,4-tetrahydroquinoline (py-THQL) using density functional theory calculations and microkinetic modeling.

The selection of TiO₂ phase, whether anatase or rutile, for supporting small Ni clusters significantly influences the activity and selectivity in CO₂ hydrogenation to methane.

The effect of catalyst synthesis and reaction conditions on catalytic activity were accurately predicted with an interpretable data-driven strategy. The method is demonstrated for CO₂ methanation and is extendable to other catalytic processes.

Digitalisation in experimental catalysis research: we are introducing machine-readable standard operating procedures combined with automated data acquisition, storage and sharing to improve research efficiency and reproducibility.

Vibrational frequencies can be utilized as a reference to assess the reliability of the exchange–correlation functionals.

A predictive model was developed, predicting the activation energy of the beta-scission in four different zeolite frameworks.

This work uses newly developed machine learning potentials to predict how germanium distributes within the zeolite catalysts, depending on both germanium content and the framework topology, aiding the rational zeolite design.

Removing calculations with surface reconstructions reduces the MAEs of the MLPs.

The structural patterns and catalytic activities of the surface atoms of simulated metal nanoparticles are characterised by an automatable data-driven unsupervised machine learning approach.

The oxidation of p-xylene to terephtalic acid is important for the production of polyethylene terephthalate (PET). This work investigates the coordination of the Mn catalyst species under operating conditions.

Ontology learning and named entity recognition are used to automate text data extraction from catalysis research and organizing it into a knowledge graph. Extending the CatalysisIE model practical use of the workflow for researchers is demonstrated.

Thanks to Machine Learning Perturbation Theory, a combination of AIMD with RPA was made to accurately predict the activation energy of alkene isomerization into Brønsted acidic zeolite.

Molecular 3-point turns are seen in molecular dynamics simulations of methanol and promoters of the CH₃OH to CH₃OCH₃ reaction. The more catalytically active aromatic aldehydes limit methanol diffusion less than other promoters.

A detailed understanding of the molecular diffusion in zeolite frameworks is crucial for analysing the factors controlling their catalytic performance in alkenes.

The mechanism of propene hydroformylation is studied with quantum chemistry and kinetic modelling. This yields detailed insight into mechanisms, and reveals the essential role of a complex between hydridocobalttricabonyl and toluene.