| Training machine-learning potentials for crystal structure prediction using disordered structures C Hong, JM Choi, W Jeong, S Kang, S Ju, K Lee, J Jung, Y Youn, S Han Physical Review B 102 (22), 224104, 2020 | 31 | 2020 |
| Efficient atomic-resolution uncertainty estimation for neural network potentials using a replica ensemble W Jeong, D Yoo, K Lee, J Jung, S Han The Journal of Physical Chemistry Letters 11 (15), 6090-6096, 2020 | 27 | 2020 |
| Metadynamics sampling in atomic environment space for collecting training data for machine learning potentials D Yoo, J Jung, W Jeong, S Han npj Computational Materials 7 (1), 131, 2021 | 15 | 2021 |
| AMP2: A fully automated program for ab initio calculations of crystalline materials Y Youn, M Lee, C Hong, D Kim, S Kim, J Jung, K Yim, S Han Computer Physics Communications 256, 107450, 2020 | 11 | 2020 |
| Stability and equilibrium structures of unknown ternary metal oxides explored by machine-learned potentials S Hwang, J Jung, C Hong, W Jeong, S Kang, S Han Journal of the American Chemical Society 145 (35), 19378-19386, 2023 | 10 | 2023 |
| Atomistic kinetic Monte Carlo simulation on atomic layer deposition of TiN thin film S Kim, H An, S Oh, J Jung, B Kim, SK Nam, S Han Computational Materials Science 213, 111620, 2022 | 8 | 2022 |
| Applications and training sets of machine learning potentials C Hong, J Kim, J Kim, J Jung, S Ju, JM Choi, S Han Science and Technology of Advanced Materials: Methods 3 (1), 2269948, 2023 | 6 | 2023 |
| Electrochemical Degradation of Pt3Co Nanoparticles Investigated by Off-Lattice Kinetic Monte Carlo Simulations with Machine-Learned Potentials J Jung, S Ju, P Kim, D Hong, W Jeong, J Lee, S Han, S Kang ACS Catalysis 13 (24), 16078-16087, 2023 | 4 | 2023 |
| Disorder-Dependent Li Diffusion in Li6PS5Cl Investigated by Machine-Learning Potential J Lee, S Ju, S Hwang, J You, J Jung, Y Kang, S Han ACS Applied Materials & Interfaces, 2024 | 2 | 2024 |
| Predicting melting temperature of inorganic crystals via crystal graph neural network enhanced by transfer learning J Kim, J Jung, S Kim, S Han Computational Materials Science 234, 112783, 2024 | 2 | 2024 |
| Modified Activation-Relaxation Technique (ARTn) Method Tuned for Efficient Identification of Transition States in Surface Reactions J Jung, H An, J Lee, S Han Journal of Chemical Theory and Computation, 2024 | | 2024 |
| Disorder-dependent Li diffusion in investigated by machine learning potential J Lee, S Ju, S Hwang, J You, J Jung, Y Kang, S Han arXiv preprint arXiv:2310.19350, 2023 | | 2023 |
| Constructing the Neural Network Potential With the Energies of the Atom and Its Derivatives J Jung, W Jeong, S Han Bulletin of the American Physical Society 65, 2020 | | 2020 |
Seungwu HanSeoul National University在 snu.ac.kr 的电子邮件经过验证
