| Machine learning meets volcano plots: computational discovery of cross-coupling catalysts B Meyer, B Sawatlon, S Heinen, OA Von Lilienfeld, C Corminboeuf Chemical science 9 (35), 7069-7077, 2018 | 217 | 2018 |
| Thousands of reactants and transition states for competing E2 and S2 reactions GF von Rudorff, SN Heinen, M Bragato, OA von Lilienfeld Machine Learning: Science and Technology 1 (4), 045026, 2020 | 63 | 2020 |
| Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space S Heinen, GF von Rudorff, OA von Lilienfeld The Journal of Chemical Physics 155 (6), 2021 | 58 | 2021 |
| Machine learning the computational cost of quantum chemistry S Heinen, M Schwilk, GF von Rudorff, OA von Lilienfeld Machine Learning: Science and Technology 1 (2), 025002, 2020 | 46 | 2020 |
| Kernel based quantum machine learning at record rate: Many-body distribution functionals as compact representations D Khan, S Heinen, OA von Lilienfeld The Journal of Chemical Physics 159 (3), 2023 | 18 | 2023 |
| Transition state search and geometry relaxation throughout chemical compound space with quantum machine learning S Heinen, GF von Rudorff, OA von Lilienfeld The Journal of Chemical Physics 157 (22), 2022 | 15 | 2022 |
| Reducing training data needs with minimal multilevel machine learning (M3L) S Heinen, D Khan, GF von Rudorff, K Karandashev, DJA Arrieta, ... Machine Learning: Science and Technology 5 (2), 025058, 2024 | 9 | 2024 |
| Crash testing machine learning force fields for molecules, materials, and interfaces: Molecular dynamics in the TEA challenge 2023 I Poltavsky, M Puleva, A Charkin-Gorbulin, G Fonseca, I Batatia, ... Chemical Science, 2025 | 6 | 2025 |
| OA vonLilienfeld, C. Corminboeuf B Meyer, B Sawatlon, S Heinen Chem. Sci 9, 7069-7077, 2018 | 5 | 2018 |
| The quantum chemical search for novel materials and the issue of data processing: The InfoMol project HP Lüthi, S Heinen, G Schneider, A Glöss, MP Brändle, RA King, ... Journal of Computational Science 15, 65-73, 2016 | 5 | 2016 |
| Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials S Lee, S Heinen, D Khan, OA von Lilienfeld Machine Learning: Science and Technology 5 (1), 015052, 2024 | 4 | 2024 |
| Evolutionary Monte Carlo of QM properties in chemical space: Electrolyte design K Karandashev, J Weinreich, S Heinen, DJ Arismendi Arrieta, ... Journal of Chemical Theory and Computation 19 (23), 8861-8870, 2023 | 2 | 2023 |
| Combining Hammett σ constants for Δ-machine learning and catalyst discovery VD Rakotonirina, M Bragato, S Heinen, OA von Lilienfeld Digital Discovery 3 (12), 2487-2496, 2024 | 1 | 2024 |
| Quantum Machine Learning Applied to Chemical Reaction Space S Heinen University_of_Basel, 2021 | 1 | 2021 |
| Optimizing woodcutting with zirconia-toughened alumina: Processing, performance, and industrial insights T Thakur, S Heinen, B Ehrle, G Blugan Heliyon 11 (2), 2025 | | 2025 |
| Combination rule for Hammett constants in computational catalyst discovery VD Rakotonirina, M Bragato, S Heinen, OA von Lilienfeld arXiv preprint arXiv:2405.07747, 2024 | | 2024 |
| Revolutionizing Wood Cutting: Zirconia Toughened Alumina-Sro Advancements SN Heinen, T Thakur, B Ehrle, G Blugan Available at SSRN 4895387, 0 | | |
O. Anatole von LilienfeldClark & CIFAR AI chair at University of Toronto/Vector Institute/Technical University Berlin確認したメール アドレス: utoronto.ca