4 citations · 5 across the 3 of their papers we have counts for
3 papers
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…
Scalable Machine Learning Model for Energy Decomposition Analysis in Aqueous Systems
Hossein Tahmasbi, Michael Beerbaum, Bartosz Brzoza +2
Energy decomposition analysis (EDA) based on absolutely localized molecular orbitals provides detailed insights into intermolecular bonding by decomposing the total molecular bindi…
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…