most citedBenchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems

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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…