| Inverse design of porous materials using artificial neural networks B Kim, S Lee, J Kim Science advances 6 (1), eaax9324, 2020 | 328 | 2020 |
| Applications of machine learning in metal-organic frameworks S Chong, S Lee, B Kim, J Kim Coordination Chemistry Reviews 423, 213487, 2020 | 163 | 2020 |
| Computational screening of trillions of metal–organic frameworks for high-performance methane storage S Lee, B Kim, H Cho, H Lee, SY Lee, ES Cho, J Kim ACS Applied Materials & Interfaces 13 (20), 23647-23654, 2021 | 156 | 2021 |
| Text mining metal–organic framework papers S Park, B Kim, S Choi, PG Boyd, B Smit, J Kim Journal of chemical information and modeling 58 (2), 244-251, 2018 | 55 | 2018 |
| Predicting performance limits of methane gas storage in zeolites with an artificial neural network S Lee, B Kim, J Kim Journal of Materials Chemistry A 7 (6), 2709-2716, 2019 | 48 | 2019 |
| Discovery of record-breaking metal-organic frameworks for methane storage using evolutionary algorithm and machine learning S Lee, B Kim, J Kim | 4 | 2020 |
| Data-Driven Design of Flexible Metal–Organic Frameworks for Gas Storage Y Lim, B Kim, J Kim Chemistry of Materials, 2024 | 3 | 2024 |
