| 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 | 664* | 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 | 465 | 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 | 439 | 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 | 396 | 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), 2020 | 282 | 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 | 125 | 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 | 88 | 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 | 68 | 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 | 55 | 2021 |
| 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 | 33 | 2022 |
| GPU-accelerated approximate kernel method for quantum machine learning NJ Browning, FA Faber, O Anatole von Lilienfeld The Journal of Chemical Physics 157 (21), 2022 | 10 | 2022 |
| Predictive Minisci late stage functionalization with transfer learning E King-Smith, FA Faber, U Reilly, AV Sinitskiy, Q Yang, B Liu, D Hyek, ... Nature Communications 15 (1), 426, 2024 | 6* | 2024 |
| Modeling materials quantum properties with machine learning FA Faber, O Anatole von Lilienfeld Materials Informatics: Methods, Tools and Applications, 171-179, 2019 | 5 | 2019 |
| Quantum machine learning with response operators in chemical compound space FA Faber, AS Christensen, OA Lilienfeld Machine Learning Meets Quantum Physics, 155-169, 2020 | 4 | 2020 |
| 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 | 3 | 2022 |
| Wyckoff Set Regression for Materials Discovery REA Goodall, AS Parackal, FA Faber, R Armiento Neural Information Processing Systems 7, 2020 | 3 | 2020 |
| Equivariant matrix function neural networks I Batatia, LL Schaaf, H Chen, G Csányi, C Ortner, FA Faber arXiv preprint arXiv:2310.10434, 2023 | 2 | 2023 |
| Quantum machine learning in chemical space FA Faber University_of_Basel, 2019 | 1 | 2019 |
| Identifying Crystal Structures from XRD Data using Enumeration Beyond Known Prototypes AS Parackal, REA Goodall, FA Faber, R Armiento arXiv preprint arXiv:2309.16454, 2023 | | 2023 |
| Identifying Crystal Structures Beyond Known Prototypes from X-ray Powder Diffraction Spectra AS Parackal, REA Goodall, FA Faber, R Armiento arXiv e-prints, arXiv: 2309.16454, 2023 | | 2023 |
O. Anatole von LilienfeldClark & CIFAR AI chair at University of Toronto/Vector Institute/Technical University Berlinutoronto.ca üzerinde doğrulanmış e-posta adresine sahip
