3 papers
cond-mat.mtrl-sci2026
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 DreÃler
Universal machine-learning interatomic potentials (MLIPs) are rapidly becoming general-purpose tools for atomistic simulation, but their role in quantitative materials modeling whe…
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
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…