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Felix A Faber

Showing results (1-10 of 9) with videos related to

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The Journal of Chemical Physics|February 17, 2019
Operators in quantum machine learning: Response properties in chemical spaceAnders S Christensen, Felix A Faber, O Anatole von Lilienfeld
The Journal of Chemical Physics|December 13, 2022
GPU-accelerated approximate kernel method for quantum machine learningNicholas J Browning, Felix A Faber, O Anatole von Lilienfeld
Physical Review Letters|October 8, 2016
Machine Learning Energies of 2 Million Elpasolite (ABC_{2}D_{6}) CrystalsFelix A Faber, Alexander Lindmaa, O Anatole von Lilienfeld, et al.
The Journal of Chemical Physics|July 2, 2018
Alchemical and structural distribution based representation for universal quantum machine learningFelix A Faber, Anders S Christensen, Bing Huang, et al.
The Journal of Chemical Physics|February 3, 2020
FCHL revisited: Faster and more accurate quantum machine learningAnders S Christensen, Lars A Bratholm, Felix A Faber, et al.
Science Advances|July 27, 2022
Rapid discovery of stable materials by coordinate-free coarse grainingRhys E A Goodall, Abhijith S Parackal, Felix A Faber, et al.
Nature Communications|January 15, 2024
Predictive Minisci late stage functionalization with transfer learningEmma King-Smith, Felix A Faber, Usa Reilly, et al.
Journal of Chemical Theory and Computation|September 20, 2017
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT ErrorFelix A Faber, Luke Hutchison, Bing Huang, et al.
Nature Communications|August 26, 2025
Predictive design of crystallographic chiral separationRokas Elijošius, Emma King-Smith, Felix A Faber, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
The Journal of Chemical Physics|February 17, 2019
Operators in quantum machine learning: Response properties in chemical spaceAnders S Christensen, Felix A Faber, O Anatole von Lilienfeld
The Journal of Chemical Physics|December 13, 2022
GPU-accelerated approximate kernel method for quantum machine learningNicholas J Browning, Felix A Faber, O Anatole von Lilienfeld
Physical Review Letters|October 8, 2016
Machine Learning Energies of 2 Million Elpasolite (ABC_{2}D_{6}) CrystalsFelix A Faber, Alexander Lindmaa, O Anatole von Lilienfeld, et al.
The Journal of Chemical Physics|July 2, 2018
Alchemical and structural distribution based representation for universal quantum machine learningFelix A Faber, Anders S Christensen, Bing Huang, et al.
The Journal of Chemical Physics|February 3, 2020
FCHL revisited: Faster and more accurate quantum machine learningAnders S Christensen, Lars A Bratholm, Felix A Faber, et al.
Science Advances|July 27, 2022
Rapid discovery of stable materials by coordinate-free coarse grainingRhys E A Goodall, Abhijith S Parackal, Felix A Faber, et al.
Nature Communications|January 15, 2024
Predictive Minisci late stage functionalization with transfer learningEmma King-Smith, Felix A Faber, Usa Reilly, et al.
Journal of Chemical Theory and Computation|September 20, 2017
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT ErrorFelix A Faber, Luke Hutchison, Bing Huang, et al.
Nature Communications|August 26, 2025
Predictive design of crystallographic chiral separationRokas Elijošius, Emma King-Smith, Felix A Faber, et al.
Pageof 1