| Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package E Epifanovsky, ATB Gilbert, X Feng, J Lee, Y Mao, N Mardirossian, ... The Journal of chemical physics 155 (8), 2021 | 642 | 2021 |
| The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics K Yao, JE Herr, DW Toth, R Mckintyre, J Parkhill Chemical science 9 (8), 2261-2269, 2018 | 440 | 2018 |
| Kinetic energy of hydrocarbons as a function of electron density and convolutional neural networks K Yao, J Parkhill Journal of chemical theory and computation 12 (3), 1139-1147, 2016 | 128 | 2016 |
| Growth of single-and bilayer ZnO on Au (111) and interaction with copper X Deng, K Yao, K Sun, WX Li, J Lee, C Matranga The Journal of Physical Chemistry C 117 (21), 11211-11218, 2013 | 128 | 2013 |
| Intrinsic bond energies from a bonds-in-molecules neural network K Yao, JE Herr, SN Brown, J Parkhill The journal of physical chemistry letters 8 (12), 2689-2694, 2017 | 122 | 2017 |
| The many-body expansion combined with neural networks K Yao, JE Herr, J Parkhill The Journal of chemical physics 146 (1), 2017 | 111 | 2017 |
| Efficient exploration of chemical space with docking and deep learning Y Yang, K Yao, MP Repasky, K Leswing, R Abel, BK Shoichet, SV Jerome Journal of Chemical Theory and Computation 17 (11), 7106-7119, 2021 | 110 | 2021 |
| Metadynamics for training neural network model chemistries: A competitive assessment JE Herr, K Yao, R McIntyre, DW Toth, J Parkhill The Journal of chemical physics 148 (24), 2018 | 72 | 2018 |
| Machine learning for vibrational spectroscopic maps AA Kananenka, K Yao, SA Corcelli, JL Skinner Journal of Chemical Theory and Computation 15 (12), 6850-6858, 2019 | 64 | 2019 |
| Detection of electron tunneling across plasmonic nanoparticle–film junctions using nitrile vibrations H Wang, K Yao, JA Parkhill, ZD Schultz Physical Chemistry Chemical Physics 19 (8), 5786-5796, 2017 | 33 | 2017 |
| Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences JE Herr, K Koh, K Yao, J Parkhill The Journal of chemical physics 151 (8), 2019 | 29 | 2019 |
| Epik: pKa and Protonation State Prediction through Machine Learning RC Johnston, K Yao, Z Kaplan, M Chelliah, K Leswing, S Seekins, ... Journal of chemical theory and computation 19 (8), 2380-2388, 2023 | 20 | 2023 |
| On the border between localization and delocalization: tris (iminoxolene) titanium (IV) T Marshall-Roth, K Yao, JA Parkhill, SN Brown Dalton Transactions 48 (4), 1427-1435, 2019 | 12 | 2019 |
| First-principles study of water activation on Cu-ZnO catalysts K Yao, SS Wang, XK Gu, HY Su, WX Li Chinese Journal of Catalysis 34 (9), 1705-1711, 2013 | 11 | 2013 |
| Compressing physical properties of atomic species for improving predictive chemistry JE Herr, K Koh, K Yao, J Parkhill arXiv preprint arXiv:1811.00123, 2018 | 1 | 2018 |
| Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package W Skomorowski, PR Horn, AF White, P Pokhilko, N Mardirossian, Y Mao, ... Journal of Chemical Physics, 2021 | | 2021 |
