| Maximizing the energy density and stability of Ni-rich layered cathode materials with multivalent dopants via machine learning M Kim, S Kang, HG Park, K Park, K Min Chemical Engineering Journal 452, 139254, 2023 | 32 | 2023 |
| Accelerated discovery of novel garnet-type solid-state electrolyte candidates via machine learning J Sun, S Kang, J Kim, K Min ACS Applied Materials & Interfaces 15 (4), 5049-5057, 2023 | 20 | 2023 |
| Machine Learning-Aided Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries S Kang, M Kim, K Min arXiv preprint arXiv:2202.06763, 2022 | 9 | 2022 |
| Screening Platform for Promising Na Superionic Conductors for Na-Ion Solid-State Electrolytes J Kim, S Kang, K Min ACS Applied Materials & Interfaces 15 (35), 41417-41425, 2023 | 8 | 2023 |
| Discovery of superionic solid-state electrolyte for Li-ion batteries via machine learning S Kang, M Kim, K Min The Journal of Physical Chemistry C 127 (39), 19335-19343, 2023 | 5 | 2023 |
| Toward fast and accurate machine learning interatomic potentials for atomic layer deposition precursors S Kang, J Kim, T Park, J Won, C Baik, J Han, K Min Materials Today Advances 21, 100474, 2024 | 2 | 2024 |
| Prediction of protein aggregation propensity via Data-Driven approaches S Kang, M Kim, J Sun, M Lee, K Min ACS Biomaterials Science & Engineering 9 (11), 6451-6463, 2023 | 2 | 2023 |
| Integrating Data Mining and Natural Language Processing to Construct a Melting Point Database for Organometallic Compounds J Jeong, T Park, JH Song, S Kang, J Won, J Han, K Min Journal of Chemical Information and Modeling, 2024 | | 2024 |
| Interpretable machine learning boosting the discovery of targeted organometallic compounds with optimal bandgap T Park, JH Song, J Jeong, S Kang, J Kim, J Won, J Han, K Min Materials Today Advances 23, 100520, 2024 | | 2024 |
