O. Anatole von Lilienfeld

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Alexandre TkatchenkoProfessor of Physics, University of Luxembourg; Visiting Professor, TU Berlin; APS Fellow; FRSCVerified email at uni.lu
Matthias RuppLuxembourg Institute of Science and Technology (LIST)Verified email at list.lu
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Raghunathan RamakrishnanTata Institute of Fundamental Research Hyderabad, IndiaVerified email at tifrh.res.in
Guido Falk von RudorffDepartment of Chemistry, University KasselVerified email at uni-kassel.de
Huang BingWuhan UniversityVerified email at whu.edu.cn
Felix Andreas FaberResearcher in Data Science and Modelling, AstraZenecaVerified email at astrazeneca.com
Ursula RothlisbergerProfessor of Computational Chemistry and BiochemistryVerified email at epfl.ch
Anders S. ChristensenQuantum Consulting by ChristensenVerified email at unibas.ch
Ivano TavernelliIBM Quantum, IBM Research - ZurichVerified email at zurich.ibm.com
Stefan HeinenPostdoc, Empa, High Performance Ceramics LabVerified email at empa.ch
Pavlo O. DralProfessor, Xiamen UniversityVerified email at xmu.edu.cn
Daniel SebastianiProfessor of Theoretical Chemistry, University Halle, GermanyVerified email at chemie.uni-halle.de
Grégoire MontavonGuest professor, Freie Universität BerlinVerified email at fu-berlin.de
Mark E TuckermanNew York UniversityVerified email at nyu.edu
Louis-Francois ArsenaultColumbia universityVerified email at columbia.edu
Dominik LemmDepartment of Physics, University of ViennaVerified email at univie.ac.at
Alejandro Lopez-BezanillaStaff Scientist at Los Alamos Nat'l LabVerified email at lanl.gov
Mauricio D. Coutinho-NetoUniversidade Federal do ABCVerified email at ufabc.edu.br
Rickard ArmientoLinköping UniversityVerified email at liu.se

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	2548	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	2146	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	903	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	892	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	808	2013
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	733*	2017
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	725	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	663	2004
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	516	2015
Long range interactions in nanoscale science RH French, VA Parsegian, R Podgornik, RF Rajter, A Jagota, J Luo, ... Reviews of Modern Physics 82 (2), 1887-1944, 2010	489	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	481	2016
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	432	2018
Exploring chemical compound space with quantum-based machine learning OA von Lilienfeld, KR Müller, A Tkatchenko Nature Reviews Chemistry 4 (7), 347-358, 2020	349	2020
Retrospective on a decade of machine learning for chemical discovery OA von Lilienfeld, K Burke Nature communications 11 (1), 4895, 2020	343	2020
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	336	2020
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	335	2016
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	313	2015
Quantum machine learning in chemical compound space OA Von Lilienfeld Angewandte Chemie International Edition 57 (16), 4164-4169, 2018	271	2018
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	258	2015
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	255	2015

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