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20202026
most citedPerformance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces

112 citations · 421 across the 14 of their papers we have counts for

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cond-mat.mtrl-sci2026

Cross-Geometry Transferability Assessment of Universal Machine Learning Interatomic Potentials: From Bulk Materials to Atomic Nanowires

Pedro H. M. Zanineli, Bruno Focassio, Gabriel R. Schleder

Foundation machine-learning interatomic potentials (MLIPs) enable atomistic simulations at substantially lower computational cost than first-principles methods, but their reliabili…