5 citations · 10 across the 8 of their papers we have counts for
11 papers
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…
Enabling Domain-Specific Atomistic Models: A Machine Learning Potential for the Solid Acid Family
Jonas Hänseroth, Rose Asuka Baroness von Stackelberg, Christian Dreßler
Machine-learned interatomic potentials trained across the periodic table have made atomistic simulation broadly accessible, and specializing them to a single compound class is wide…
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…
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…
Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models
Jonas Hänseroth, Aaron Flötotto, Christian Dreßler
Universal machine-learning interatomic potentials (MLIPs) are rapidly becoming general-purpose tools for atomistic simulation, but their role in quantitative materials modeling whe…
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…
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…
Variational quantum algorithm for anion exchange across electrolyzer membrane
Timur Gubaev, Philipp Pfeffer, Christian Dreßler +1
We present a variational quantum algorithm that solves the one-dimensional diffusion problem with a space-dependent diffusion constant . This problem is relevant for the exch…
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…
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…
Acid Base Chemistry of Short Hydrogen Bonds: A Tale of Schrödinger's Cat in Glutamine-Derived Crystals
Muhammad Nawaz Qaisrani, Nandha Kumar, Christian Dreßler +2
Short hydrogen bonds (SHBs), defined by donor-acceptor distances below 2.5 Angstrom, represent a distinct regime in acid-base chemistry where conventional models of hydrogen bondin…