203 citations · 219 across the 4 of their papers we have counts for
Showing cond-mat.mtrl-sciShow all
2 papers · 1 filter
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…
cond-mat.mtrl-sci2025★ 203 cited
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…