5 citations · 10 across the 8 of their papers we have counts for
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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…
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…