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

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

collaborators

3 papers

cond-mat.mtrl-sci2025

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials

Jaesun Kim, Jinmu You, Yutack Park +11

Accurate yet transferable machine-learning interatomic potentials (MLIPs) are essential for accelerating materials and chemical discovery. However, most universal MLIPs overfit to…

cond-mat.mtrl-sci2025★ 1 cited

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials

Jisu Kim, Jiho Lee, Sangmin Oh +5

Pretrained universal machine-learning interatomic potentials (MLIPs) have revolutionized computational materials science by enabling rapid atomistic simulations as efficient altern…

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…