| Novel high-κ dielectrics for next-generation electronic devices screened by automated ab initio calculations K Yim, Y Yong, J Lee, K Lee, HH Nahm, J Yoo, C Lee, C Seong Hwang, ... NPG Asia Materials 7 (6), e190-e190, 2015 | 249 | 2015 |
| SIMPLE-NN: An efficient package for training and executing neural-network interatomic potentials K Lee, D Yoo, W Jeong, S Han Computer Physics Communications 242, 95-103, 2019 | 141 | 2019 |
| Bridging TCAD and AI: Its application to semiconductor design C Jeong, S Myung, I Huh, B Choi, J Kim, H Jang, H Lee, D Park, K Lee, ... IEEE Transactions on Electron Devices 68 (11), 5364-5371, 2021 | 45 | 2021 |
| Toward reliable and transferable machine learning potentials: uniform training by overcoming sampling bias W Jeong, K Lee, D Yoo, D Lee, S Han The Journal of Physical Chemistry C 122 (39), 22790-22795, 2018 | 45 | 2018 |
| Atomic energy mapping of neural network potential D Yoo, K Lee, W Jeong, D Lee, S Watanabe, S Han Physical Review Materials 3 (9), 093802, 2019 | 39 | 2019 |
| 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 | 28 | 2020 |
| Identification of ground-state spin ordering in antiferromagnetic transition metal oxides using the Ising model and a genetic algorithm K Lee, Y Youn, S Han Science and Technology of advanced MaTerialS 18 (1), 246-252, 2017 | 21 | 2017 |
Seungwu HanSeoul National UniversityПодтвержден адрес электронной почты в домене snu.ac.kr
