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20242026
most citedA practical guide to machine learning interatomic potentials -- Status and future

203 citations · 219 across the 4 of their papers we have counts for

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cond-mat.mtrl-sci2026★ 1 cited

Bias in Universal Machine-Learned Interatomic Potentials and its Effects on Fine-Tuning

Nicolas Wong, Julia H. Yang

Universal machine learned interatomic potentials (uMLIPs) embody a growing area of interest due to their transferability across the periodic table, displaying an error of about 0.6…