O. Anatole von Lilienfeld

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Alexandre TkatchenkoProfessor of Physics, University of Luxembourg; Visiting Professor, TU BerlinVerified email at uni.lu
Klaus-Robert MüllerTU Berlin & Korea University & Google Deepmind & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Matthias RuppLuxembourg Institute of Science and Technology (LIST)Verified email at list.lu
Raghunathan RamakrishnanAssociate Professor, Tata Institute of Fundamental Research, Hyderabad, IndiaVerified email at tifrh.res.in
Huang BingUniversity of ViennaVerified email at univie.ac.at
Felix Andreas FaberSNSF early postdoc fellow at the University of CambridgeVerified email at cam.ac.uk
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
Stefan HeinenPostdoc, Vector InstituteVerified email at vectorinstitute.ai
Pavlo O. DralProfessor, Xiamen UniversityVerified email at xmu.edu.cn
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

Clark & CIFAR AI chair at 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	2029	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	1409	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	746	2015
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	683	2015
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	672	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	641	2013
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	637	2004
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	583	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	444	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	417	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	407	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), 2018	364	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), 2016	296	2016
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	241	2020
Retrospective on a decade of machine learning for chemical discovery OA von Lilienfeld, K Burke Nature communications 11 (1), 4895, 2020	236	2020
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	236	2012
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	234	2015
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), 2010	233	2010
Quantum machine learning in chemical compound space OA Von Lilienfeld Angewandte Chemie International Edition 57 (16), 4164-4169, 2018	231	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), 2015	226	2015

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