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

112 citations · 112 across the 1 of their papers we have counts for

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cond-mat.mtrl-sci2024★ 112 cited

Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces

Bruno Focassio, Luis Paulo Mezzina Freitas, Gabriel R. Schleder

Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency…