O. Anatole von Lilienfeld

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Alexandre TkatchenkoProfessor of Physics, University of Luxembourg; Visiting Professor, TU BerlinVerified email at uni.lu
Matthias RuppLuxembourg Institute of Science and Technology (LIST)Verified email at list.lu
Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Raghunathan RamakrishnanTata Institute of Fundamental Research, Hyderabad, IndiaVerified email at tifrh.res.in
Felix Andreas FaberSNSF early postdoc fellow at the University of CambridgeVerified email at cam.ac.uk
Huang BingUniversity of ViennaVerified email at univie.ac.at
Guido Falk von RudorffDepartment of Chemistry, University KasselVerified email at uni-kassel.de
Ursula RothlisbergerProfessor of Computational Chemistry and BiochemistryVerified email at epfl.ch
Anders S. ChristensenQuantum Consulting by ChristensenVerified email at unibas.ch
ivano tavernelliIBM Research - Zurich (ita'at'zurich.ibm.com)Verified email at zurich.ibm.com
Daniel SebastianiProfessor of Theoretical Chemistry, University Halle, GermanyVerified email at chemie.uni-halle.de
Pavlo O. DralProfessor, Xiamen UniversityVerified email at xmu.edu.cn
Stefan HeinenPostdoc, Vector InstituteVerified email at vectorinstitute.ai
Mauricio D. Coutinho-NetoUniversidade Federal do ABCVerified email at ufabc.edu.br
Grégoire MontavonFreie Universität BerlinVerified email at fu-berlin.de
Mark E TuckermanNew York UniversityVerified email at nyu.edu
Louis-Francois ArsenaultColumbia universityVerified email at columbia.edu
Alejandro Lopez-BezanillaStaff Scientist at Los Alamos Nat'l LabVerified email at lanl.gov
Rickard ArmientoLinköping UniversityVerified email at liu.se
Alexander LindmaaRacah Institute of Physics, Hebrew University of JerusalemVerified email at mail.huji.ac.il

O. Anatole von Lilienfeld

University of Toronto/Vector Institute/Technical University Berlin

Verified email at utoronto.ca - Homepage

computational physics theoretical chemistry quantum machine learning density functional theory chemical space


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Fast and accurate modeling of molecular atomization energies with machine learning M Rupp, A Tkatchenko, KR Müller, OA Von Lilienfeld Physical review letters 108 (5), 058301, 2012	1854	2012
Quantum chemistry structures and properties of 134 kilo molecules R Ramakrishnan, PO Dral, M Rupp, OA Von Lilienfeld Scientific data 1 (1), 1-7, 2014	1186	2014
Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space K Hansen, F Biegler, R Ramakrishnan, W Pronobis, OA Von Lilienfeld, ... The journal of physical chemistry letters 6 (12), 2326-2331, 2015	682	2015
Optimization of effective atom centered potentials for London dispersion forces in density functional theory OA Von Lilienfeld, I Tavernelli, U Rothlisberger, D Sebastiani Physical review letters 93 (15), 153004, 2004	632	2004
Machine learning of molecular electronic properties in chemical compound space G Montavon, M Rupp, V Gobre, A Vazquez-Mayagoitia, K Hansen, ... New Journal of Physics 15 (9), 095003, 2013	629	2013
Assessment and validation of machine learning methods for predicting molecular atomization energies K Hansen, G Montavon, F Biegler, S Fazli, M Rupp, M Scheffler, ... Journal of Chemical Theory and Computation 9 (8), 3404-3419, 2013	612	2013
Big data meets quantum chemistry approximations: the Δ-machine learning approach R Ramakrishnan, PO Dral, M Rupp, OA Von Lilienfeld Journal of chemical theory and computation 11 (5), 2087-2096, 2015	600	2015
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 13 (11), 5255-5264, 2017	529	2017
Long range interactions in nanoscale science RH French, VA Parsegian, R Podgornik, RF Rajter, A Jagota, J Luo, ... Reviews of Modern Physics 82 (2), 1887, 2010	434	2010
Machine Learning Energies of 2 Million Elpasolite Crystals FA Faber, A Lindmaa, OA Von Lilienfeld, R Armiento Physical review letters 117 (13), 135502, 2016	385	2016
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	375	2015
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	334	2018
Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity B Huang, OA Von Lilienfeld The Journal of Chemical Physics 145 (16), 161102, 2016	271	2016
Two-and three-body interatomic dispersion energy contributions to binding in molecules and solids O Anatole von Lilienfeld, A Tkatchenko The Journal of chemical physics 132 (23), 234109, 2010	227	2010
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties OA Von Lilienfeld, R Ramakrishnan, M Rupp, A Knoll International Journal of Quantum Chemistry 115 (16), 1084-1093, 2015	225	2015
Collective many-body van der Waals interactions in molecular systems RA DiStasio Jr, OA von Lilienfeld, A Tkatchenko Proceedings of the National Academy of Sciences 109 (37), 14791-14795, 2012	224	2012
Machine learning for quantum mechanical properties of atoms in molecules M Rupp, R Ramakrishnan, OA Von Lilienfeld The Journal of Physical Chemistry Letters 6 (16), 3309-3313, 2015	213	2015
Quantum machine learning in chemical compound space OA Von Lilienfeld Angewandte Chemie International Edition 57 (16), 4164-4169, 2018	210	2018
Electronic spectra from TDDFT and machine learning in chemical space R Ramakrishnan, M Hartmann, E Tapavicza, OA Von Lilienfeld The Journal of chemical physics 143 (8), 084111, 2015	208	2015
Retrospective on a decade of machine learning for chemical discovery OA von Lilienfeld, K Burke Nature communications 11 (1), 4895, 2020	200	2020

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