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20232026
most citedGPTFF: A high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials

47 citations · 100 across the 15 of their papers we have counts for

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cond-mat.mtrl-sci2025★ 1 cited

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential

Hanwen Kang, Tenglong Lu, Zhanbin Qi +3

We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine learning force fields (MLFFs) with 3D potential energy s…