| 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 | 1804 | 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 | 1134 | 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 | 666 | 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 | 627 | 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 | 616 | 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 | 603 | 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 | 571 | 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 | 517 | 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 | 432 | 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 | 377 | 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 | 362 | 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 | 323 | 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 | 261 | 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 | 226 | 2010 |
| 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 |
| 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 | 221 | 2015 |
| 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 | 212 | 2015 |
| Quantum machine learning in chemical compound space OA Von Lilienfeld Angewandte Chemie International Edition 57 (16), 4164-4169, 2018 | 203 | 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 | 202 | 2015 |
| Library of dispersion-corrected atom-centered potentials for generalized gradient approximation functionals: Elements H, C, N, O, He, Ne, Ar, and Kr IC Lin, MD Coutinho-Neto, C Felsenheimer, OA von Lilienfeld, I Tavernelli, ... Physical Review B 75 (20), 205131, 2007 | 195 | 2007 |
Alexandre TkatchenkoProfessor of Physics, University of Luxembourg; Visiting Professor, TU BerlinVerified email at uni.lu
