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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-sci2024

Data-efficient multi-fidelity training for high-fidelity machine learning interatomic potentials

Jaesun Kim, Jisu Kim, Jaehoon Kim +4

Machine learning interatomic potentials (MLIPs) are used to estimate potential energy surfaces (PES) from ab initio calculations, providing near quantum-level accuracy with reduced…