From the 1 of 10 linked papers with an AI index.
10 papers
Self-organized defect clustering and concentration-dependent vacancy diffusion in MoS
Aaron Flötotto, Benjamin Spetzler, Martin Ziegler +2
The paper uses kinetic Monte‑Carlo simulations, informed by machine‑learning interatomic potentials, to study how sulfur vacancies in MoS₂ cluster and diffuse, revealing concentrat…
Quantum nuclear and band-dispersion effects recover near-UV absorption in short-hydrogen-bonded organic crystals
Jonas Hänseroth, Max GroÃmann, Malte Grunert +4
Near-UV optical absorption is increasingly reported in hydrogen-bonded organic and biomolecular materials lacking aromatic or extended pi-conjugated chromophores, yet its microscop…
High-throughput screening and mechanistic insights into solid acid proton conductors
Jonas Hänseroth, Max GroÃmann, Malte Grunert +2
Proton-conducting solid acids could enable water-free operation of high-temperature fuel cells. However, systematic materials screening has, hitherto, been computationally prohibit…
Broken neural scaling laws in materials science
Max GroÃmann, Max Großmann, Malte Grunert +1
In materials science, data are scarce and expensive to generate, whether computationally or experimentally. Therefore, it is crucial to identify how model performance scales with d…
Large-scale cooperative sulfur vacancy dynamics in two-dimensional MoS2 from machine learning interatomic potentials
Aaron Flötotto, Benjamin Spetzler, Rose von Stackelberg +3
The formation of extended sulfur vacancies in MoS2 monolayers is closely associated with catalytic activity and may also be the basis for its memristive behavior. Nanosecond-scale…
Many-body perturbation theory vs. density functional theory: A systematic benchmark for band gaps of solids
Max GroÃmann, Marc Thieme, Malte Grunert +1
We benchmark many-body perturbation theory against density functional theory (DFT) for the band gaps of solids. We systematically compare four variants using…