Luuk H. E. Kempen

most citedBreaking scaling relations with inverse catalysts: a machine learning exploration of trends in

C O_{2}

hydrogenation energy barriers

4 citations · 7 across the 3 of their papers we have counts for

collaborators

Showing cond-mat.mtrl-sciShow all

3 papers · 1 filter

cond-mat.mtrl-sci2025★ 1 cited

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★ 2 cited

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 $C O_{2}$ to value-added compounds is an important part of the effort to store and reuse atmospheric $C O_{2}$ emissions. Here we focus on $\mathrm{CO…

cond-mat.mtrl-sci2025★ 4 cited

Breaking scaling relations with inverse catalysts: a machine learning exploration of trends in $C O_{2}$ hydrogenation energy barriers

Luuk H. E. Kempen, Marius Juul Nielsen, Mie Andersen

The conversion of $C O_{2}$ into useful products such as methanol is a key strategy for abating climate change and our dependence on fossil fuels. Developing new catalysts fo…

How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?

Interpretable machine learned predictions of adsorption energies at the metal--oxide interface

Breaking scaling relations with inverse catalysts: a machine learning exploration of trends in CO2​ hydrogenation energy barriers

Breaking scaling relations with inverse catalysts: a machine learning exploration of trends in $C O_{2}$ hydrogenation energy barriers