Molecular insight into catalysis

Computational Heterogeneous Catalysis for sustainable chemical conversion.

Computational chemistry helps rationalize, understand, and unravel heterogeneous catalysis by connecting atomistic structure, active sites, reaction mechanisms, and kinetic models across scales.

Atomistic nanoparticle model

Research

01 / 04

Multiscale and data-driven modelling

Link atoms, sites, particles, and reactors.

DFT energetics, microkinetic models, reactor simulations, descriptors, and machine-learning potentials make it possible to follow catalytic behavior across length and time scales. Interpretable modelling identifies which elementary steps, coverages, and environments control performance.

Publication highlights

Selected publication highlights

Selected paper

Transferable, Living Data Sets for Predicting Global Minimum Energy Nanocluster Geometries. details JCTC 2024, 20 (15), 6801-6812.

Selected paper

Unravelling CO Activation on Flat and Stepped Co Surfaces: A Molecular Orbital Analysis. details J. Phys. Chem. C 2024, 128 (22), 8947-8960.

Selected paper

Unravelling the Role of Metal-Support Interactions on the Structure Sensitivity of Fischer-Tropsch Synthesis. details J. Phys. Chem. C 2023, 127 (31), 15148–15156.

Selected paper

Enumerating active sites on metal nanoparticles: Understanding the size dependence of cobalt particles for CO dissociation. details ACS Catal. 2021, 11 (14), 8484-8492.

Recent output

Latest publications

Published

Neural-network analysis of CO adlayer structures and lateral interactions on transition metal surfaces. details AI for Science 2026, 2 (2), 025001.

Published

Correlating Vibrational Spectra to C1 Surface Species on Nickel-Based Hydrogenation Catalysts Using Density Functional Theory. details Chem. Eur. J. 2026, 4 (4), e202500338.

Published

Microkinetics of CO2 hydrogenation to methanol on In2O3-supported Ni-In clusters. details Appl. Catal. B 2026, 384, 126238.

Published

Catalytic In Situ Acetalization Strategy for High-Yield Synthesis of C4- and C2-Carbohydrate Derivatives from Glucose. details ACS Catal. 2025, 16 (2), 1675-1683.

Published

PyQInt: A Teaching-Oriented Hartree–Fock Implementation in Python. details JOSE 2025, 8 (94), 286.

Published

A hybrid machine learning and DFT-informed microkinetic model for ammonia decomposition on Ru/Al2O3. details Chem. Eng. J. 2025, 525 (1), 169993.

Networks and programmes

Projects, Consortia & Collaborations