Felix Andreas Faber
Felix Andreas Faber
SNSF early postdoc fellow at the University of Cambridge
Verified email at cam.ac.uk
Title
Cited by
Cited by
Year
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
4262017
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
2992016
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
2962015
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
2382018
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
1102020
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
722019
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
392017
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
282020
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
232017
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
212021
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
32019
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
22020
Operators in machine learning: Response properties in chemical space
AS Christensen, FA Faber, OA von Lilienfeld
arXiv preprint arXiv:1807.08811, 0
2
Wyckoff Set Regression for Materials Discovery
REA Goodall, AS Parackal, FA Faber, R Armiento
Neural Information Processing Systems, 7, 2020
12020
Quantum machine learning in chemical space
FA Faber
University_of_Basel, 2019
12019
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
The system can't perform the operation now. Try again later.
Articles 1–17