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Felix Andreas Faber
Felix Andreas Faber
Researcher in Data Science and Modelling, AstraZeneca
Verified email at astrazeneca.com
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
684*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
4842015
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
4502016
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
4032018
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
2952020
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
1282019
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
732017
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
722020
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
592021
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
342022
QML: A Python toolkit for quantum machine learning, 2017
AS Christensen, FA Faber, B Huang, LA Bratholm, A Tkatchenko, ...
URL https://github. com/qmlcode/qml, 0
28
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
10*2024
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
92022
Quantum machine learning with response operators in chemical compound space
FA Faber, AS Christensen, OA Lilienfeld
Machine Learning Meets Quantum Physics, 155-169, 2020
42020
Modeling materials quantum properties with machine learning
FA Faber, O Anatole von Lilienfeld
Materials Informatics: Methods, Tools and Applications, 171-179, 2019
42019
Equivariant matrix function neural networks
I Batatia, LL Schaaf, H Chen, G Csányi, C Ortner, FA Faber
arXiv preprint arXiv:2310.10434, 2023
32023
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
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
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