Jonas Hänseroth

collaborators

3 papers

cond-mat.mtrl-sci2026

Data-Efficient Construction of Material-Specific Machine-Learning Interatomic Potentials from Ab Initio Molecular Dynamics Trajectories

Jonas Hänseroth, Christian Dreßler

Pretrained machine-learning interatomic potentials, so-called universal or foundation models offer an appealing starting point for atomistic simulations, but their accuracy for mat…

physics.comp-ph2025

Hydroxide Mobility in Aqueous Systems: Ab Initio Accuracy with Millisecond Timescales

Jonas HÃ¤nseroth, Daniel Sebastiani, Jakob Scholl +2

We present a multiscale simulation approach for hydroxide transport in aqueous solutions of potassium hydroxide, combining ab initio molecular dynamics (AIMD) simulations with forc…

physics.comp-ph2025

Modelling complex proton transport phenomena -- Exploring the limits of fine-tuning and transferability of foundational machine-learned force fields

Malte Grunert, Max GroÃmann, Jonas HÃ¤nseroth +5

The solid acids CsH $_{2}$ PO $_{4}$ and Cs $_{7}$ (H $_{4}$ PO $_{4}$ )(H $_{2}$ PO $_{4}$ ) $_{8}$ pose significant challenges for the simulation of proton transport phenomena. In this work, we use the recent…