| 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 | 350* | 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 | 241 | 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 | 232 | 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 | 167 | 2018 |
| 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 | 59 | 2019 |
| 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 | 49 | 2020 |
| 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 | 27 | 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 | 11 | 2020 |
| QML: A Python Toolkit for Quantum Machine Learning, 2019 AS Christensen, LA Bratholm, S Amabilino, JC Kromann, FA Faber, ... | 8 | |
| 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, 2020 | 5 | 2020 |
| 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 | 1 | 2020 |
| Quantum machine learning in chemical space FA Faber University_of_Basel, 2019 | | 2019 |
O. Anatole von LilienfeldProfessor of Computational Materials Discovery, Faculty of Physics, University of Viennaunivie.ac.at üzerinde doğrulanmış e-posta adresine sahip
