Author

Christian Dreßler

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Preprint Sep 2026

Enabling Domain-Specific Atomistic Models: A Machine Learning Potential for the Solid Acid Family

Machine-learned interatomic potentials trained across the periodic table have made atomistic simulation broadly accessible, and specializing them to a single compound class is widely expected to improve accuracy. Yet examples remain scarce, and fewer still surpass universal models in speed or reach a higher level of el...

Jonas Hänseroth, Rose von Stackelberg, Christian Dreßler · 0 citations

Preprint Sep 2026

Reference-free certification of machine-learning interatomic potentials

Universal machine-learning interatomic potentials now reach held-out energy and force errors so small that they no longer predict how a model behaves in simulation. Here we show that a potential can be graded without any reference calculation, against properties the exact Born-Oppenheimer surface satisfies by mathemati...

Jonas Hanseroth, Christian Dreßler · 0 citations

Preprint Aug 2026

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

Pretrained machine-learning interatomic potentials, so-called universal or foundation models offer an appealing starting point for atomistic simulations, but their accuracy for material-specific observables often remains limited without additional reference data (fine-tuning). Here, we systematically quantify how much...

Jonas Hänseroth, Christian Dreßler · 2 citations