4 citations · 9 across the 5 of their papers we have counts for
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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 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★ 4 cited
Transition States Energies from Machine Learning: An Application to Reverse Water-Gas Shift on Single-Atom Alloys
Raffaele Cheula, Mie Andersen
Obtaining accurate transition state (TS) energies is a bottleneck in computational screening of complex materials and reaction networks due to the high cost of TS search methods an…