| Understanding the diversity of the metal-organic framework ecosystem SM Moosavi, A Nandy, KM Jablonka, D Ongari, JP Janet, PG Boyd, Y Lee, ... Nature Communications 11, 4068, 2020 | 493 | 2020 |
| 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 | 221 | 2021 |
| 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), 2913-2922, 2019 | 221 | 2019 |
| 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 | 168 | 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 | 130 | 2021 |
| Highly effective ammonia removal in a series of Brønsted acidic porous polymers: investigation of chemical and structural variations G Barin, GW Peterson, V Crocella, J Xu, KA Colwell, A Nandy, JA Reimer, ... Chemical Science 8 (6), 4399-4409, 2017 | 110 | 2017 |
| 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, 2019 | 106 | 2019 |
| 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 | 95 | 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 | 91 | 2019 |
| 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 | 84 | 2022 |
| Effect of hydration on healthy intervertebral disk mechanical stiffness SE Bezci, A Nandy, GD O'Connell Journal of Biomechanical Engineering 137 (10), 101007, 2015 | 84 | 2015 |
| 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 | 69 | 2020 |
| 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 | 58 | 2022 |
| 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 | 55 | 2021 |
| NMR spectroscopy reveals adsorbate binding sites in the metal–organic framework UiO-66 (Zr) A Nandy, AC Forse, VJ Witherspoon, JA Reimer The Journal of Physical Chemistry C 122 (15), 8295-8305, 2018 | 54 | 2018 |
| 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 | 51 | 2022 |
| 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 | 46 | 2021 |
| 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 | 45 | 2020 |
| Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts A Nandy, H Kulik ACS Catalysis, 2020 | 44 | 2020 |
| A database of ultrastable MOFs reassembled from stable fragments with machine learning models A Nandy, S Yue, C Oh, C Duan, GG Terrones, YG Chung, HJ Kulik Matter 6 (5), 1585-1603, 2023 | 39 | 2023 |
Heather J. KulikChemical Engineering and Chemistry, Massachusetts Institute of TechnologyEmail verificata su mit.edu