6 papers
Fine-tuning universal machine learning potentials for transition state search in surface catalysis
Raffaele Cheula, Mie Andersen, John R. Kitchin
Determining transition states (TSs) of surface reactions is central to understanding and designing heterogeneous catalysts but remains computationally prohibitive with density func…
Machine learning exploration of binding energy distributions of H2O at astrochemically relevant dust grain surfaces
Anant Vaishnav, Niels M. Mikkelsen, Mie Andersen
Binding energies (BEs) of adsorbates on interstellar dust grains critically control adsorption, desorption, diffusion, and surface reactivity, and therefore strongly influence astr…
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…
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…
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…
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…