2 citations · 3 across the 4 of their papers we have counts for
5 papers
Assessing the Transferability of General-Purpose MachineLearning Interatomic Potentials for Heterogeneous Catalysis with HetCat26
Alexandre Peuch, Giaan Kler-Young, Kaifeng Niu +4
Foundation machine learning interatomic potentials (MLIPs) promise near-density functional theory (DFT) accuracy across broad areas of chemistry and materials science. However, the…
Breaking Water at Graphene Defects
Samuel G. H. Brookes, IniOluwa C. Popoola, Fabian Berger +3
Water dissociation at solid surfaces underpins processes ranging from corrosion and catalysis to electrochemistry and photovoltaics. Defects often serve as reactive sites for disso…
Mechanisms for the Formation of Active Sites in Single-Atom Alloys
Ioannis Karageorgiou, Angelos Michaelides, Fabian Berger
Reactive dopant atoms embedded in inert host metal surfaces define the active sites in single-atom alloys (SAAs), yet SAA synthesis remains challenging. To address this, we elucida…
How Accurate Are DFT Forces? Unexpectedly Large Uncertainties in Molecular Datasets
Domantas Kuryla, Fabian Berger, Gábor Csányi +1
Training of general-purpose machine learning interatomic potentials (MLIPs) relies on large datasets with properties usually computed with density functional theory (DFT). A pre-re…
Cooperative CO capture via oxalate formation on metal-decorated graphene
Inioluwa Christianah Popoola, Benjamin Xu Shi, Fabian Berger +5
CO capture using carbon-based materials, particularly graphene and graphene-like materials, is a promising strategy to deal with CO emissions. However, significant gaps rem…