most citedMachine Learning-Driven Structure Prediction for Iron Hydrides

4 citations · 5 across the 3 of their papers we have counts for

collaborators
Showing cond-mat.mtrl-sciShow all

2 papers · 1 filter

cond-mat.mtrl-sci2025★ 1 cited

Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems

Hossein Tahmasbi, Andreas Knüpfer, Thomas D. Kühne +1

The rapid emergence of universal Machine Learning Interatomic Potentials (uMLIPs) has transformed materials modeling. However, a comprehensive understanding of their generalization…

cond-mat.mtrl-sci2023★ 4 cited

Machine Learning-Driven Structure Prediction for Iron Hydrides

Hossein Tahmasbi, Kushal Ramakrishna, Mani Lokamani +1

We created a computational workflow to analyze the potential energy surface (PES) of materials using machine-learned interatomic potentials in conjunction with the minima hopping a…