| Quantum chemistry structures and properties of 134 kilo molecules R Ramakrishnan, PO Dral, M Rupp, OA Von Lilienfeld Scientific data 1, 140022, 2014 | 2146 | 2014 |
| Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach R Ramakrishnan, PO Dral, M Rupp, OA von Lilienfeld Journal of Chemical Theory and Computation 11 (5), 2087-2096, 2015 | 892 | 2015 |
| Quantum Chemistry in the Age of Machine Learning PO Dral The Journal of Physical Chemistry Letters 11 (6), 2336-2347, 2020 | 406 | 2020 |
| Molecular excited states through a machine learning lens PO Dral, M Barbatti Nature Reviews Chemistry 5 (6), 388-405, 2021 | 173 | 2021 |
| Deep Learning for Nonadiabatic Excited-State Dynamics WK Chen, XY Liu, WH Fang, PO Dral, G Cui The journal of physical chemistry letters 9 (23), 6702-6708, 2018 | 165 | 2018 |
| Semiempirical quantum-chemical orthogonalization-corrected methods: theory, implementation, and parameters PO Dral, X Wu, L Spörkel, A Koslowski, W Weber, R Steiger, M Scholten, ... Journal of chemical theory and computation 12 (3), 1082-1096, 2016 | 165 | 2016 |
| Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels PO Dral, A Owens, SN Yurchenko, W Thiel The Journal of Chemical Physics 146 (24), 244108, 2017 | 152 | 2017 |
| Nonadiabatic Excited-State Dynamics with Machine Learning PO Dral, M Barbatti, W Thiel The journal of physical chemistry letters 9 (19), 5660-5663, 2018 | 151 | 2018 |
| Choosing the right molecular machine learning potential M Pinheiro Jr, F Ge, N Ferré, PO Dral, M Barbatti Chemical Science, 2021 | 146 | 2021 |
| Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations PO Dral, OA von Lilienfeld, W Thiel Journal of Chemical Theory and Computation 11 (5), 2120-2125, 2015 | 125 | 2015 |
| The relationship between threshold voltage and dipolar character of self-assembled monolayers in organic thin-film transistors M Salinas, CM Jäger, AY Amin, PO Dral, T Meyer-Friedrichsen, A Hirsch, ... Journal of the American Chemical Society 134 (30), 12648-12652, 2012 | 119 | 2012 |
| Semiempirical quantum-chemical orthogonalization-corrected methods: benchmarks for ground-state properties PO Dral, X Wu, L Spörkel, A Koslowski, W Thiel Journal of chemical theory and computation 12 (3), 1097-1120, 2016 | 104 | 2016 |
| Artificial intelligence-enhanced quantum chemical method with broad applicability P Zheng, R Zubatyuk, W Wu, O Isayev, PO Dral Nature communications 12 (1), 7022, 2021 | 94 | 2021 |
| Hierarchical machine learning of potential energy surfaces PO Dral, A Owens, A Dral, G Csányi The Journal of Chemical Physics 152 (20), 204110, 2020 | 91 | 2020 |
| Semiempirical quantum-chemical methods with Orthogonalization and dispersion corrections PO Dral, X Wu, W Thiel Journal of chemical theory and computation 15 (3), 1743-1760, 2019 | 79 | 2019 |
| MLatom: A program package for quantum chemical research assisted by machine learning PO Dral Journal of Computational Chemistry 40 (26), 2339-2347, 2019 | 71 | 2019 |
| Newton-X Platform: New Software Developments for Surface Hopping and Nuclear Ensembles M Barbatti, M Bondanza, R Crespo-Otero, B Demoulin, PO Dral, ... Journal of Chemical Theory and Computation 18 (11), 6851-6865, 2022 | 62 | 2022 |
| Machine Learning for Absorption Cross Sections BX Xue, M Barbatti, PO Dral The Journal of Physical Chemistry A 124 (35), 7199-7210, 2020 | 62 | 2020 |
| Oxygen-Doped Nanodiamonds: Synthesis and Functionalizations† AA Fokin, TS Zhuk, AE Pashenko, PO Dral, PA Gunchenko, JEP Dahl, ... Organic letters 11 (14), 3068-3071, 2009 | 61 | 2009 |
| MLatom 2: An Integrative Platform for Atomistic Machine Learning PO Dral, F Ge, BX Xue, YF Hou, M Pinheiro, J Huang, M Barbatti Topics in Current Chemistry 379 (4), 1-41, 2021 | 60 | 2021 |
O. Anatole von LilienfeldClark & CIFAR AI chair at University of Toronto/Vector Institute/Technical University BerlinVerified email at utoronto.ca