1 citations · 1 across the 1 of their papers we have counts for
3 papers
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…
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…