3 papers
cond-mat.mtrl-sci2026
Chemo-mechanical coupling stabilizes mixed solar-cell absorbers: Insights from Monte-Carlo simulations assisted by ab initio informed machine-learning potentials
Vasilios Karanikolas, Delwin Perera, Linus Erhard +2
Alloying Ag into Cu(In,Ga)Se has enabled record solar-cell efficiencies (), yet their long-term stability remains in question because initio calculations predict a…
cond-mat.mtrl-sci2025
How Realistic are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper-Water Interfaces
Linus C. Erhard, Johannes Schörghuber, Aleix Comas-Vives +1
Copper is a highly promising catalyst for the electrochemical CO reduction reaction (CO2RR) since it is the only pure metal that can form highly added-value products such as et…
cond-mat.mtrl-sci2025
Machine-learning interatomic potentials from a users perspective: A comparison of accuracy, speed and data efficiency
Niklas Leimeroth, Linus C. Erhard, Karsten Albe +1
Machine learning interatomic potentials (MLIPs) have massively changed the field of atomistic modeling. They enable the accuracy of density functional theory in large-scale simulat…