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20232026
most citedAn efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials

1 citations · 2 across the 6 of their papers we have counts for

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cond-mat.mtrl-sci2026★ 1 cited

A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations

Sangmin Oh, Jinmu You, Jaesun Kim +4

We introduce a lightweight universal machine-learning interatomic potential (uMLIP), SevenNet-Nano, based on the graph neural network architecture SevenNet and enabled by a knowled…