| Prediction errors of molecular machine learning models lower than hybrid DFT error FA Faber, L Hutchison, B Huang, J Gilmer, SS Schoenholz, GE Dahl, ... Journal of Chemical Theory and Computation, 2017 | 526 | 2017 |
| Crystal structure representations for machine learning models of formation energies F Faber, A Lindmaa, OA von Lilienfeld, R Armiento International Journal of Quantum Chemistry 115 (16), 1094-1101, 2015 | 381 | 2015 |
| Machine Learning Energies of 2 Million Elpasolite (A B C 2 D 6) Crystals FA Faber, A Lindmaa, OA von Lilienfeld, R Armiento Physical Review Letters 117 (13), 135502, 2016 | 380 | 2016 |
| Alchemical and structural distribution based representation for universal quantum machine learning FA Faber, AS Christensen, B Huang, OA von Lilienfeld The Journal of Chemical Physics 148 (24), 241717, 2018 | 334 | 2018 |
| FCHL revisited: faster and more accurate quantum machine learning AS Christensen, LA Bratholm, FA Faber, O Anatole von Lilienfeld The Journal of Chemical Physics 152 (4), 044107, 2020 | 198 | 2020 |
| Operators in quantum machine learning: Response properties in chemical space AS Christensen, FA Faber, OA von Lilienfeld The Journal of Chemical Physics 150 (6), 064105, 2019 | 104 | 2019 |
| QML: A Python toolkit for quantum machine learning AS Christensen, FA Faber, B Huang, LA Bratholm, A Tkatchenko, ... URL https://github. com/qmlcode/qml, 2017 | 70 | 2017 |
| Neural networks and kernel ridge regression for excited states dynamics of CH2NH: From single-state to multi-state representations and multi-property machine learning models J Westermayr, FA Faber, AS Christensen, OA von Lilienfeld, ... Machine Learning: Science and Technology 1 (2), 025009, 2020 | 50 | 2020 |
| An assessment of the structural resolution of various fingerprints commonly used in machine learning B Parsaeifard, DS De, AS Christensen, FA Faber, E Kocer, S De, J Behler, ... Machine Learning: Science and Technology 2 (1), 015018, 2021 | 44 | 2021 |
| Fast machine learning models of electronic and energetic properties consistently reach approximation errors better than DFT accuracy FA Faber, L Hutchison, B Huang, J Gilmer, SS Schoenholz, GE Dahl, ... arXiv preprint arXiv:1702.05532, 2017 | 21 | 2017 |
| Rapid discovery of stable materials by coordinate-free coarse graining REA Goodall, AS Parackal, FA Faber, R Armiento, AA Lee Science Advances 8 (30), eabn4117, 2022 | 11* | 2022 |
| Modeling Materials Quantum Properties with Machine Learning FA Faber, O Anatole von Lilienfeld Materials Informatics: Methods, Tools and Applications, 171-179, 2019 | 4 | 2019 |
| Wyckoff Set Regression for Materials Discovery REA Goodall, AS Parackal, FA Faber, R Armiento Neural Information Processing Systems 7, 2020 | 3 | 2020 |
| GPU-accelerated approximate kernel method for quantum machine learning NJ Browning, FA Faber, O Anatole von Lilienfeld The Journal of Chemical Physics 157 (21), 214801, 2022 | 2 | 2022 |
| Quantum machine learning with response operators in chemical compound space FA Faber, AS Christensen, O Lilienfeld Machine Learning Meets Quantum Physics, 155-169, 2020 | 2 | 2020 |
| Quantum machine learning in chemical space FA Faber University_of_Basel, 2019 | 1 | 2019 |
| Screening the unexplored crystal prototype space and inverting XRD patterns with the WREN machine-learning model R Armiento, A Parackal, R Goodall, F Faber Bulletin of the American Physical Society, 2023 | | 2023 |
| Exploring undiscovered crystal prototype space for XRD inversion using WREN machine learning model. A Parackal, R Armiento, R Goodall, F Faber Bulletin of the American Physical Society, 2023 | | 2023 |
| Predictive Minisci and P450 Late Stage Functionalization with Transfer Learning E King-Smith, FA Faber, AV Sinitskiy, Q Yang, B Liu, D Hyek | | 2022 |
| BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale C Poelking, FA Faber, B Cheng Machine Learning: Science and Technology 3 (4), 040501, 2022 | | 2022 |
O. Anatole von LilienfeldUniversity of Toronto/Vector Institute/Technical University BerlinVerified email at utoronto.ca
