77 citations · 107 across the 10 of their papers we have counts for
10 papers
Fermi-Level-Dependent Defect Chemistry and Oxygen Evolution Reaction Activity of Fe-Doped and Oxygen-Deficient \ce{SrTiO3}(001)
Amit Sehrawat, Jochen Rohrer
The oxygen evolution reaction (OER) on perovskite oxides is controlled by the interplay of dopant chemistry, defect charge states, and surface segregation, yet these factors are ra…
Electron Polaron at Neutral 180 Domain Wall in PbTiO: Stability, Trapping Energies, and Transverse Polarization
Mohammad Amirabbasi, Jochen Rohrer, Karsten Albe
We use density-functional theory with a Hubbard correction to investigate Ti-centered electron polarons at neutral PbO-centered domain walls in tetragonal PbTiO.…
Why hole polaron formation on oxygen is limiting the Fermi level in Fe acceptor doped BaTiO under oxidizing conditions
Mohammad Amirabbasi, Emre Erdem, Denis Sudarikov +3
Oxidizing Fe-doped BaTiO is commonly expected to convert substitutional Fe acceptors into formal Fe centers. Yet, the experimentally accessible picture based on e…
First-principles investigation of small polarons in rhombohedral NaNbO
Mohammad Amirabbasi, Lorenzo Villa, Elaheh Ghorbani +2
Sodium niobate (NaNbO) is a perovskite oxide and a key component of emerging lead-free antiferroelectric capacitors for high-energy-density applications. However, its perform…
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…
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…
Structure-property relations of silicon oxycarbides studied using a machine learning interatomic potential
Niklas Leimeroth, Jochen Rohrer, Karsten Albe
Silicon oxycarbides show outstanding versatility due to their highly tunable composition and microstructure. Consequently, a key challenge is a thorough knowledge of structure-prop…
From electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron
Sarath Menon, Yury Lysogorskiy, Alexander L. M. Knoll +10
We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning P…
Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine learning
Linus C. Erhard, Jochen Rohrer, Karsten Albe +1
Silicon-oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond…