18 citations · 44 across the 7 of their papers we have counts for
7 papers
Quantum nuclear and band-dispersion effects recover near-UV absorption in short-hydrogen-bonded organic crystals
Jonas Hänseroth, Max Großmann, 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 Großmann, 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 Großmann, Malte Grunert, Erich Runge
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…
Many-body perturbation theory vs. density functional theory: A systematic benchmark for band gaps of solids
Max Großmann, 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…
Discovery of sustainable energy materials via the machine-learned material space
Malte Grunert, Max Großmann, Erich Runge
Does a machine learning model actually gain an understanding of the material space? We answer this question in the affirmative on the example of the OptiMate model, a graph attenti…
Modelling complex proton transport phenomena -- Exploring the limits of fine-tuning and transferability of foundational machine-learned force fields
Malte Grunert, Max Großmann, Jonas Hänseroth +5
The solid acids CsHPO and Cs(HPO)(HPO) pose significant challenges for the simulation of proton transport phenomena. In this work, we use the recent…
Deep learning of spectra: Predicting the dielectric function of semiconductors
Malte Grunert, Max Großmann, Erich Runge
Predicting spectra and related properties such as the dielectric function of crystalline materials based on machine learning has a huge, hitherto unexplored, technological potentia…