Inverse design of solid-state materials via a continuous representation J Noh, J Kim, HS Stein, B Sanchez-Lengeling, JM Gregoire, ... Matter 1 (5), 1370-1384, 2019 | 334 | 2019 |
Machine learning for renewable energy materials GH Gu, J Noh, I Kim, Y Jung Journal of Materials Chemistry A 7 (29), 17096-17117, 2019 | 285 | 2019 |
Generative Adversarial Networks for Crystal Structure Prediction S Kim¢Ó, J Noh¢Ó, GH Gu, A Aspuru-Guzik, Y Jung ACS Central Science 6 (8), 1412-1420 (¢Óequal contribution), 2020 | 218 | 2020 |
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties Z Ren, SIP Tian, J Noh, F Oviedo, G Xing, J Li, Q Liang, R Zhu, AG Aberle, ... Matter 5 (1), 314-335, 2022 | 170* | 2022 |
Machine-enabled inverse design of inorganic solid materials: promises and challenges J Noh, GH Gu, S Kim, Y Jung Chemical Science 11 (19), 4871-4881, 2020 | 161 | 2020 |
Active learning with non-ab initio input features toward efficient CO 2 reduction catalysts J Noh, S Back, J Kim, Y Jung Chemical science 9 (23), 5152-5159, 2018 | 120 | 2018 |
Understanding potential-dependent competition between electrocatalytic dinitrogen and proton reduction reactions C Choi, GH Gu, J Noh, HS Park, Y Jung Nature Communications 12 (1), 4353, 2021 | 116 | 2021 |
Structure-based synthesizability prediction of crystals using partially supervised learning J Jang, GH Gu, J Noh, J Kim, Y Jung Journal of the American Chemical Society 142 (44), 18836-18843, 2020 | 107 | 2020 |
Practical deep-learning representation for fast heterogeneous catalyst screening GH Gu¢Ó, J Noh¢Ó, S Kim, S Back, Z Ulissi, Y Jung The Journal of Physical Chemistry Letters 11 (9), 3185-3191 (¢Óequal ¡¦, 2020 | 96 | 2020 |
Progress in computational and machine‐learning methods for heterogeneous small‐molecule activation GH Gu, C Choi, Y Lee, AB Situmorang, J Noh, YH Kim, Y Jung Advanced materials 32 (35), 1907865, 2020 | 62 | 2020 |
Uncertainty-quantified hybrid machine learning/density functional theory high throughput screening method for crystals J Noh, GH Gu, S Kim, Y Jung Journal of Chemical Information and Modeling 60 (4), 1996-2003, 2020 | 53 | 2020 |
Autobifunctional mechanism of jagged Pt nanowires for hydrogen evolution kinetics via end-to-end simulation GH Gu, J Lim, C Wan, T Cheng, H Pu, S Kim, J Noh, C Choi, J Kim, ... Journal of the American Chemical Society 143 (14), 5355-5363, 2021 | 42 | 2021 |
Accelerated chemical science with AI S Back, A Aspuru-Guzik, M Ceriotti, G Gryn'ova, B Grzybowski, GH Gu, ... Digital Discovery 3 (1), 23-33, 2024 | 38 | 2024 |
Perovskite synthesizability using graph neural networks GH Gu¢Ó, J Jang¢Ó, J Noh¢Ó, A Walsh, Y Jung npj Computational Materials 8 (1), 1-8 (¢Óequal contribution), 2022 | 35 | 2022 |
Unveiling new stable manganese based photoanode materials via theoretical high-throughput screening and experiments J Noh, S Kim, G ho Gu, A Shinde, L Zhou, JM Gregoire, Y Jung Chemical Communications 55 (89), 13418-13421, 2019 | 25 | 2019 |
Bimetallic Gold–Silver Nanostructures Drive Low Overpotentials for Electrochemical Carbon Dioxide Reduction JW Park¢Ó, W Choi¢Ó, J Noh¢Ó, W Park, GH Gu, J Park, Y Jung, H Song ACS Applied Materials & Interfaces 14 (5), 6604-6614 (¢Óequal contribution), 2022 | 18 | 2022 |
Path-aware and structure-preserving generation of synthetically accessible molecules J Noh, DW Jeon, K Kim, SH Han, M Lee, Y Jung Proceddings of the 39th International Conference on Machine Learning 162 ¡¦, 2022 | 10 | 2022 |
Predicting Potentially Hazardous Chemical Reactions Using Explainable Neural Network J Kim¢Ó, G Gu¢Ó, J Noh¢Ó, S Kim, S Gim, J Choi, Y Jung Chemical Sciecne 12, 11028-11037, 2021 | 6 | 2021 |
Machine learning-enabled chemical space exploration of all-inorganic perovskites for photovoltaics JS Kim, J Noh, J Im npj Computational Materials 10 (1), 97, 2024 | 5 | 2024 |
Synthesizability of materials stoichiometry using semi-supervised learning J Jang, J Noh, L Zhou, GH Gu, JM Gregoire, Y Jung Matter 7 (6), 2294-2312, 2024 | 4 | 2024 |