Felix Andreas Faber
Felix Andreas Faber
SNSF early postdoc fellow at the University of Cambridge
cam.ac.uk üzerinde doğrulanmış e-posta adresine sahip
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
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
4082017
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
2942016
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
2862015
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
2292018
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
992020
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
252020
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
192021
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
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
Wyckoff Set Regression for Materials Discovery
REA Goodall, AS Parackal, FA Faber, R Armiento
Sistem, işlemi şu anda gerçekleştiremiyor. Daha sonra yeniden deneyin.
Makaleler 1–17