3 papers
cond-mat.mtrl-sci2025
How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?
Luuk H. E. Kempen, Raffaele Cheula, Mie Andersen
Foundational machine learning interatomic potentials (MLIPs) are being developed at a rapid pace, promising closer and closer approximation to ab initio accuracy. This unlocks the…
cond-mat.mtrl-sci2025
Interpretable machine learned predictions of adsorption energies at the metal--oxide interface
Marius Juul Nielsen, Luuk H. E. Kempen, Julie de Neergaard Ravn +2
The conversion of to value-added compounds is an important part of the effort to store and reuse atmospheric emissions. Here we focus on $\mathrm{CO…
cond-mat.mtrl-sci2025
Breaking scaling relations with inverse catalysts: a machine learning exploration of trends in hydrogenation energy barriers
Luuk H. E. Kempen, Marius Juul Nielsen, Mie Andersen
The conversion of into useful products such as methanol is a key strategy for abating climate change and our dependence on fossil fuels. Developing new catalysts fo…