| Density Functional Theory in Transition-Metal Chemistry: A Self-Consistent Hubbard Approach HJ Kulik, M Cococcioni, DA Scherlis, N Marzari Physical Review Letters 97 (10), 103001, 2006 | 664 | 2006 |
| 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 (1), 1-10, 2020 | 388 | 2020 |
| Protection of tissue physicochemical properties using polyfunctional crosslinkers YG Park, CH Sohn, R Chen, M McCue, DH Yun, GT Drummond, T Ku, ... Nature biotechnology 37 (1), 73-83, 2019 | 322 | 2019 |
| Critical knowledge gaps in mass transport through single-digit nanopores: a review and perspective S Faucher, N Aluru, MZ Bazant, D Blankschtein, AH Brozena, J Cumings, ... The Journal of Physical Chemistry C 123 (35), 21309-21326, 2019 | 292 | 2019 |
| Mechanically triggered heterolytic unzipping of a low-ceiling-temperature polymer CE Diesendruck, GI Peterson, HJ Kulik, JA Kaitz, BD Mar, PA May, ... Nature chemistry 6 (7), 623-628, 2014 | 244 | 2014 |
| Resolving transition metal chemical space: Feature selection for machine learning and structure–property relationships JP Janet, HJ Kulik The Journal of Physical Chemistry A 121 (46), 8939-8954, 2017 | 240 | 2017 |
| Perspective: Treating electron over-delocalization with the DFT+ U method HJ Kulik The Journal of chemical physics 142 (24), 240901, 2015 | 211 | 2015 |
| How large should the QM region be in QM/MM calculations? The case of catechol O-methyltransferase HJ Kulik, J Zhang, JP Klinman, TJ Martinez The Journal of Physical Chemistry B 120 (44), 11381-11394, 2016 | 203 | 2016 |
| Predicting electronic structure properties of transition metal complexes with neural networks JP Janet, HJ Kulik Chemical Science 8 (7), 5137-5152, 2017 | 202 | 2017 |
| Accelerating chemical discovery with machine learning: simulated evolution of spin crossover complexes with an artificial neural network JP Janet, L Chan, HJ Kulik The Journal of Physical Chemistry Letters 9 (5), 1064-1071, 2018 | 199 | 2018 |
| 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 |
| molSimplify: A toolkit for automating discovery in inorganic chemistry EI Ioannidis, TZH Gani, HJ Kulik Journal of computational chemistry 37 (22), 2106-2117, 2016 | 169 | 2016 |
| 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 |
| Understanding and Breaking Scaling Relations in Single-Site Catalysis: Methane to Methanol Conversion by FeIV=O TZH Gani, HJ Kulik ACS Catalysis 8 (2), 975-986, 2018 | 149 | 2018 |
| 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 |
| Anion‐Selective Redox Electrodes: Electrochemically Mediated Separation with Heterogeneous Organometallic Interfaces X Su, HJ Kulik, TF Jamison, TA Hatton Advanced Functional Materials 26 (20), 3394-3404, 2016 | 125 | 2016 |
| Ab initio quantum chemistry for protein structures HJ Kulik, N Luehr, IS Ufimtsev, TJ Martinez The Journal of Physical Chemistry B 116 (41), 12501-12509, 2012 | 124 | 2012 |
| Systematic study of first-row transition-metal diatomic molecules: A self-consistent approach HJ Kulik, N Marzari The Journal of chemical physics 133 (11), 114103, 2010 | 116 | 2010 |
| Towards quantifying the role of exact exchange in predictions of transition metal complex properties EI Ioannidis, HJ Kulik The Journal of chemical physics 143 (3), 034104, 2015 | 113 | 2015 |
Aditya NandySchmidt AI in Science Fellow at UChicago and Incoming Assistant Professor at UCLA (2025)在 ucla.edu 的电子邮件经过验证
