Felix Andreas Faber
Felix Andreas Faber
SNSF early postdoc fellow at the University of Cambridge
確認したメール アドレス: cam.ac.uk
タイトル
引用先
引用先
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
260*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
2022016
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
1872015
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
1012018
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
322019
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
192017
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
92020
Qml: A python toolkit for quantum machine learning. 2017
A Christensen, F Faber, B Huang, L Bratholm, A Tkatchenko, K Müller, ...
5
Neural networks and kernel ridge regression for excited states dynamics of CH2NH2+: from single-state to multi-state representations and multi-property machine …
J Westermayr, F Faber, AS Christensen, A von Lilienfeld, P Marquetand
Machine Learning: Science and Technology, 2020
22020
Modeling Materials Quantum Properties with Machine Learning
FA Faber, O Anatole von Lilienfeld
Materials Informatics: Methods, Tools and Applications, 171-179, 2019
12019
Quantum machine learning in chemical space
FA Faber
University_of_Basel, 2019
2019
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論文 1–11