| 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 | 408 | 2017 |
| 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 | 294 | 2016 |
| 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 | 286 | 2015 |
| 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 | 229 | 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 | 99 | 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 | 72 | 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 | 39 | 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 | 25 | 2020 |
| 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 | 23 | 2017 |
| 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 | 19 | 2021 |
| QML: A Python toolkit for quantum machine learning, 2017 AS Christensen, F Faber, B Huang, LA Bratholm, A Tkatchenko, K Müller, ... URL https://github. com/qmlcode/qml, 0 | 13 | |
| Modeling Materials Quantum Properties with Machine Learning FA Faber, O Anatole von Lilienfeld Materials Informatics: Methods, Tools and Applications, 171-179, 2019 | 3 | 2019 |
| Quantum Machine Learning with Response Operators in Chemical Compound Space FA Faber, AS Christensen, OA von Lilienfeld Machine Learning Meets Quantum Physics, 155-169, 2020 | 2 | 2020 |
| Operators in machine learning: Response properties in chemical space AS Christensen, FA Faber, OA von Lilienfeld arXiv preprint arXiv:1807.08811, 0 | 2 | |
| Quantum machine learning in chemical space FA Faber University_of_Basel, 2019 | 1 | 2019 |
| Rapid Discovery of Novel Materials by Coordinate-free Coarse Graining REA Goodall, AS Parackal, FA Faber, R Armiento, AA Lee arXiv preprint arXiv:2106.11132, 2021 | | 2021 |
| Wyckoff Set Regression for Materials Discovery REA Goodall, AS Parackal, FA Faber, R Armiento | | |
O. Anatole von LilienfeldUniversity of Viennaunivie.ac.at üzerinde doğrulanmış e-posta adresine sahip
