5 papers · 1 filter
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…
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…
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…
Understanding phase transitions of -quartz under dynamic compression conditions by machine-learning driven atomistic simulations
Linus C. Erhard, Christoph Otzen, Jochen Rohrer +2
Characteristic shock effects in silica serve as a key indicator of historical impacts at geological sites. Despite this geological significance, atomistic details of structural tra…
Crystal structure identification with 3D convolutional neural networks with application to high-pressure phase transitions in SiO
Linus C. Erhard, Daniel Utt, Arne J. Klomp +1
Efficient, reliable and easy-to-use structure recognition of atomic environments is essential for the analysis of atomic scale computer simulations. In this work, we train two neur…