most citedFine-Tuning Unifies Foundational Machine-learned Interatomic Potential Architectures at ab initio Accuracy

5 citations · 6 across the 4 of their papers we have counts for

collaborators

6 papers

cond-mat.mtrl-sci2026

Reference-free certification of machine-learning interatomic potentials

Jonas Hänseroth, Christian Dreßler

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 th…

physics.chem-ph2026

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…

cond-mat.mtrl-sci2026

Revealing Hydroxide Ion Transport Mechanisms in Commercial Anion-Exchange Membranes at Nano-Scale from Machine-learned Interatomic Potential Simulations

Jonas Hänseroth, Muhammad Nawaz Qaisrani, Mostafa Moradi +2

Hydroxide ion transport in anion-exchange membranes fundamentally limits the efficiency of alkaline water electrolysis for green hydrogen production, yet the atomic-scale transport…

physics.chem-ph2025

Bridging Atomistic and Mesoscale Lithium Transport via Machine-Learned Force Fields and Markov State Models

Muhammad Nawaz Qaisrani, Christoph Kirsch, Aaron Flötotto +4

Lithium diffusion in solid-state battery anodes occurs through thermally activated hops between metastable sites often separated by large energy barriers, making such events rare o…

physics.chem-ph2025★ 5 cited

Fine-Tuning Unifies Foundational Machine-learned Interatomic Potential Architectures at ab initio Accuracy

Jonas Hänseroth, Aaron Flötotto, Muhammad Nawaz Qaisrani +1

This work demonstrates that fine-tuning transforms foundational machine-learned interatomic potentials (MLIPs) to achieve consistent, near-ab initio accuracy across diverse archite…

physics.chem-ph2025★ 1 cited

Optimizing Machine Learning Potentials for Hydroxide Transport: Surprising Efficiency of Single-Concentration Training

Jonas Hänseroth, Christian Dreßler

We investigate the transferability of machine learning interatomic potentials across concentration variations in chemically similar systems, using aqueous potassium hydroxide solut…