| 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 | 121 | 2019 |
| A band-gap database for semiconducting inorganic materials calculated with hybrid functional S Kim, M Lee, C Hong, Y Yoon, H An, D Lee, W Jeong, D Yoo, Y Kang, ... Scientific Data 7 (1), 387, 2020 | 51 | 2020 |
| 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 | 44 | 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 | 36 | 2019 |
| Crystallization of amorphous GeTe simulated by neural network potential addressing medium-range order D Lee, K Lee, D Yoo, W Jeong, S Han Computational Materials Science 181, 109725, 2020 | 31 | 2020 |
| High-dimensional neural network atomic potentials for examining energy materials: some recent simulations S Watanabe, W Li, W Jeong, D Lee, K Shimizu, E Mimanitani, Y Ando, ... Journal of Physics: Energy 3 (1), 012003, 2020 | 30 | 2020 |
| 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 | 20 | 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 | 20 | 2020 |
| Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials S Kang, W Jeong, C Hong, S Hwang, Y Yoon, S Han npj Computational Materials 8 (1), 108, 2022 | 16 | 2022 |
| Accelerated computation of lattice thermal conductivity using neural network interatomic potentials JM Choi, K Lee, S Kim, M Moon, W Jeong, S Han Computational Materials Science 211, 111472, 2022 | 11 | 2022 |
| 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 | 11 | 2021 |
| Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon H Kwon, W Sun, T Hsu, W Jeong, F Aydin, S Sharma, F Meng, ... The Journal of Physical Chemistry C 127 (33), 16473-16484, 2023 | 6 | 2023 |
| Athermal glass work at the nanoscale: Engineered electron-beam-induced viscoplasticity for mechanical shaping of brittle amorphous silica SG Kang, K Jeong, J Paeng, W Jeong, S Han, JP Ahn, S Boles, HN Han, ... Acta Materialia 238, 118203, 2022 | 6 | 2022 |
| 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 | 5 | 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 | 3 | 2023 |
| Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models H Kwon, T Hsu, W Sun, W Jeong, F Aydin, J Chapman, X Chen, ... arXiv preprint arXiv:2312.05472, 2023 | 1 | 2023 |
| Integrating Machine Learning Potential and X-ray Absorption Spectroscopy for Predicting the Chemical Speciation of Disordered Carbon Nitrides W Jeong, W Sun, MF Calegari Andrade, LF Wan, TM Willey, MH Nielsen, ... Chemistry of Materials, 2024 | | 2024 |
| Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models TA Pham, H Kwon, T Hsu, W Sun, W Jeong, F Aydin, J Chapman, X Chen, ... | | 2023 |
| E-beam-enhanced solid-state mechanical amorphization of α-quartz: Reduced deformation barrier via localized excess electrons as network modifiers SG Kang, W Jeong, J Paeng, H Kim, E Lee, GS Park, S Han, HN Han, ... Materials Today 66, 62-71, 2023 | | 2023 |
| Developement of reliable neural network potential for metal–semiconductor interface reaction: case study for Ni silicidation W Jeong, D Yoo, K Lee, S Han Bulletin of the American Physical Society 65, 2020 | | 2020 |
