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Felix Andreas Faber
Felix Andreas Faber
Researcher in Data Science and Modelling, AstraZeneca
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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
664*2017
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
4672015
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
4392016
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
3982018
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), 2020
2832020
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
1252019
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
882017
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
682020
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
552021
Rapid discovery of stable materials by coordinate-free coarse graining
REA Goodall, AS Parackal, FA Faber, R Armiento, AA Lee
Science Advances 8 (30), eabn4117, 2022
332022
GPU-accelerated approximate kernel method for quantum machine learning
NJ Browning, FA Faber, O Anatole von Lilienfeld
The Journal of Chemical Physics 157 (21), 2022
102022
Predictive Minisci late stage functionalization with transfer learning
E King-Smith, FA Faber, U Reilly, AV Sinitskiy, Q Yang, B Liu, D Hyek, ...
Nature Communications 15 (1), 426, 2024
6*2024
Quantum machine learning with response operators in chemical compound space
FA Faber, AS Christensen, OA Lilienfeld
Machine Learning Meets Quantum Physics, 155-169, 2020
52020
Modeling materials quantum properties with machine learning
FA Faber, O Anatole von Lilienfeld
Materials Informatics: Methods, Tools and Applications, 171-179, 2019
52019
BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale
C Poelking, FA Faber, B Cheng
Machine Learning: Science and Technology 3 (4), 040501, 2022
32022
Wyckoff Set Regression for Materials Discovery
REA Goodall, AS Parackal, FA Faber, R Armiento
Neural Information Processing Systems 7, 2020
32020
Equivariant matrix function neural networks
I Batatia, LL Schaaf, H Chen, G Csányi, C Ortner, FA Faber
arXiv preprint arXiv:2310.10434, 2023
22023
Quantum machine learning in chemical space
FA Faber
University_of_Basel, 2019
12019
Identifying Crystal Structures from XRD Data using Enumeration Beyond Known Prototypes
AS Parackal, REA Goodall, FA Faber, R Armiento
arXiv preprint arXiv:2309.16454, 2023
2023
Identifying Crystal Structures Beyond Known Prototypes from X-ray Powder Diffraction Spectra
AS Parackal, REA Goodall, FA Faber, R Armiento
arXiv e-prints, arXiv: 2309.16454, 2023
2023
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