Themed collection Digital Catalysis
The design and optimization of heterogeneous catalysts using computational methods
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.
Catal. Sci. Technol., 2024,14, 515-532
https://doi.org/10.1039/D3CY01160G
Vibrational frequencies utilized for the assessment of exchange–correlation functionals in the description of metal–adsorbate systems: C2H2 and C2H4 on transition-metal surfaces
Vibrational frequencies can be utilized as a reference to assess the reliability of the exchange–correlation functionals.
Catal. Sci. Technol., 2024,14, 6924-6933
https://doi.org/10.1039/D4CY00685B
Unsupervised pattern recognition on the surface of simulated metal nanoparticles for catalytic applications
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.
Catal. Sci. Technol., 2024,14, 6651-6661
https://doi.org/10.1039/D4CY01000K
Identification of Ni3Fe alloy as a candidate catalyst for quinoline selective hydrogenation with computations
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.
Catal. Sci. Technol., 2024, Advance Article
https://doi.org/10.1039/D4CY01076K
Multiscale modelling of CO2 hydrogenation of TiO2-supported Ni8 clusters: on the influence of anatase and rutile polymorphs
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.
Catal. Sci. Technol., 2024,14, 6393-6410
https://doi.org/10.1039/D4CY00586D
Accelerated design of nickel-cobalt based catalysts for CO2 hydrogenation with human-in-the-loop active machine learning
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.
Catal. Sci. Technol., 2024,14, 6307-6320
https://doi.org/10.1039/D4CY00873A
Advancing catalysis research through FAIR data principles implemented in a local data infrastructure – a case study of an automated test reactor
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.
Catal. Sci. Technol., 2024,14, 6186-6197
https://doi.org/10.1039/D4CY00693C
Predicting the effect of framework and hydrocarbon structure on the zeolite-catalyzed beta-scission
A predictive model was developed, predicting the activation energy of the beta-scission in four different zeolite frameworks.
Catal. Sci. Technol., 2024, Advance Article
https://doi.org/10.1039/D4CY00973H
Germanium distributions in zeolites derived from neural network potentials
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.
Catal. Sci. Technol., 2024,14, 5838-5853
https://doi.org/10.1039/D4CY00763H
Investigating the error imbalance of large-scale machine learning potentials in catalysis
Removing calculations with surface reconstructions reduces the MAEs of the MLPs.
Catal. Sci. Technol., 2024,14, 5899-5908
https://doi.org/10.1039/D4CY00615A
First principles investigation of manganese catalyst structure and coordination in the p-xylene oxidation process
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.
Catal. Sci. Technol., 2024,14, 5634-5643
https://doi.org/10.1039/D4CY00284A
Generating knowledge graphs through text mining of catalysis research related literature
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.
Catal. Sci. Technol., 2024,14, 5699-5713
https://doi.org/10.1039/D4CY00369A
Machine learning thermodynamic perturbation theory offers accurate activation free energies at the RPA level for alkene isomerization in zeolites
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.
Catal. Sci. Technol., 2024,14, 5314-5323
https://doi.org/10.1039/D4CY00548A
Diffusion mechanisms and preferential dynamics of promoter molecules in ZSM-5 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.
Catal. Sci. Technol., 2024,14, 3674-3681
https://doi.org/10.1039/D4CY00506F
Influence of temperatures and loadings on olefin diffusion in MFI-type zeolites in one- to three-dimensions
A detailed understanding of the molecular diffusion in zeolite frameworks is crucial for analysing the factors controlling their catalytic performance in alkenes.
Catal. Sci. Technol., 2024,14, 1902-1910
https://doi.org/10.1039/D3CY01590D
Kinetic modelling of cobalt-catalyzed propene hydroformylation: a combined ab initio and experimental fitting protocol
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.
Catal. Sci. Technol., 2024,14, 961-972
https://doi.org/10.1039/D3CY01625K
About this collection
This themed collection of Catalysis Science & Technology, Guest Edited by Wei-Xue Li (0000-0002-5043-3088) (University of Science and Technology of China), Núria López (0000-0001-9150-5941) (ICIQ, Spain) & Evgeny Pidko (0000-0001-9242-9901) (TU Delft, The Netherlands) showcases cross-disciplinary cutting-edge advances in the application of advanced data techniques to catalysis science, including artificial intelligence, machine learning, high-throughput computational methods and other data science approaches and methodologies.