Yutack Park

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

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

collaborators

2 papers

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…

physics.comp-ph2022

$Ab initio$ construction of full phase diagram of MgO-CaO eutectic system using neural network interatomic potentials

Kyeongpung Lee, Yutack Park, Seungwu Han

While several studies confirmed that machine-learned potentials (MLPs) can provide accurate free energies for determining phase stabilities, the abilities of MLPs for efficiently c…

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

Ab initio construction of full phase diagram of MgO-CaO eutectic system using neural network interatomic potentials

$Ab initio$ construction of full phase diagram of MgO-CaO eutectic system using neural network interatomic potentials