| A quantitative uncertainty metric controls error in neural network-driven chemical discovery JP Janet, C Duan, T Yang, A Nandy, HJ Kulik Chemical science 10 (34), 7913-7922, 2019 | 185 | 2019 |
| Computational discovery of transition-metal complexes: from high-throughput screening to machine learning A Nandy, C Duan, MG Taylor, F Liu, AH Steeves, HJ Kulik Chemical reviews 121 (16), 9927-10000, 2021 | 165 | 2021 |
| Accurate multiobjective design in a space of millions of transition metal complexes with neural-network-driven efficient global optimization JP Janet, S Ramesh, C Duan, HJ Kulik ACS central science 6 (4), 513-524, 2020 | 153 | 2020 |
| Strategies and software for machine learning accelerated discovery in transition metal chemistry A Nandy, C Duan, JP Janet, S Gugler, HJ Kulik Industrial & Engineering Chemistry Research 57 (42), 13973-13986, 2018 | 146 | 2018 |
| Using machine learning and data mining to leverage community knowledge for the engineering of stable metal–organic frameworks A Nandy, C Duan, HJ Kulik Journal of the American Chemical Society 143 (42), 17535-17547, 2021 | 101 | 2021 |
| Designing in the face of uncertainty: exploiting electronic structure and machine learning models for discovery in inorganic chemistry JP Janet, F Liu, A Nandy, C Duan, T Yang, S Lin, HJ Kulik Inorganic chemistry 58 (16), 10592-10606, 2019 | 95 | 2019 |
| Zero-temperature localization in a sub-Ohmic spin-boson model investigated by an extended hierarchy equation of motion C Duan, Z Tang, J Cao, J Wu Physical Review B 95 (21), 214308, 2017 | 90 | 2017 |
| Machine learning accelerates the discovery of design rules and exceptions in stable metal–oxo intermediate formation A Nandy, J Zhu, JP Janet, C Duan, RB Getman, HJ Kulik Acs Catalysis 9 (9), 8243-8255, 2019 | 86 | 2019 |
| Learning from failure: predicting electronic structure calculation outcomes with machine learning models C Duan, JP Janet, F Liu, A Nandy, HJ Kulik Journal of Chemical Theory and Computation 15 (4), 2331-2345, 2019 | 79 | 2019 |
| Seeing is believing: Experimental spin states from machine learning model structure predictions MG Taylor, T Yang, S Lin, A Nandy, JP Janet, C Duan, HJ Kulik The Journal of Physical Chemistry A 124 (16), 3286-3299, 2020 | 63 | 2020 |
| MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks A Nandy, G Terrones, N Arunachalam, C Duan, DW Kastner, HJ Kulik Scientific Data 9 (1), 74, 2022 | 57 | 2022 |
| Rapid detection of strong correlation with machine learning for transition-metal complex high-throughput screening F Liu, C Duan, HJ Kulik The journal of physical chemistry letters 11 (19), 8067-8076, 2020 | 48 | 2020 |
| Navigating transition-metal chemical space: artificial intelligence for first-principles design JP Janet, C Duan, A Nandy, F Liu, HJ Kulik Accounts of Chemical Research 54 (3), 532-545, 2021 | 46 | 2021 |
| New strategies for direct methane-to-methanol conversion from active learning exploration of 16 million catalysts A Nandy, C Duan, C Goffinet, HJ Kulik Jacs Au 2 (5), 1200-1213, 2022 | 41 | 2022 |
| Data-driven approaches can overcome the cost–accuracy trade-off in multireference diagnostics C Duan, F Liu, A Nandy, HJ Kulik Journal of Chemical Theory and Computation 16 (7), 4373-4387, 2020 | 40 | 2020 |
| Putting density functional theory to the test in machine-learning-accelerated materials discovery C Duan, F Liu, A Nandy, HJ Kulik The Journal of Physical Chemistry Letters 12 (19), 4628-4637, 2021 | 37 | 2021 |
| Semi-supervised machine learning enables the robust detection of multireference character at low cost C Duan, F Liu, A Nandy, HJ Kulik The Journal of Physical Chemistry Letters 11 (16), 6640-6648, 2020 | 34 | 2020 |
| Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery A Nandy, C Duan, HJ Kulik Current Opinion in Chemical Engineering 36, 100778, 2022 | 32 | 2022 |
| Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics A Nandy, DBK Chu, DR Harper, C Duan, N Arunachalam, Y Cytter, ... Physical Chemistry Chemical Physics 22 (34), 19326-19341, 2020 | 29 | 2020 |
| Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles C Duan, S Chen, MG Taylor, F Liu, HJ Kulik Chemical Science 12 (39), 13021-13036, 2021 | 26 | 2021 |
Heather J. KulikChemical Engineering and Chemistry, Massachusetts Institute of Technology在 mit.edu 的电子邮件经过验证
