| 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 | 2470 | 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 | 2041 | 2014 |
| 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 | 861 | 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 | 793 | 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 | 711 | 2013 |
| Finding density functionals with machine learning JC Snyder, M Rupp, K Hansen, KR Müller, K Burke Physical review letters 108 (25), 253002, 2012 | 668 | 2012 |
| Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information I Sushko, S Novotarskyi, R Körner, AK Pandey, M Rupp, W Teetz, ... Journal of computer-aided molecular design 25, 533-554, 2011 | 648 | 2011 |
| Machine learning for quantum mechanics in a nutshell M Rupp International Journal of Quantum Chemistry 115 (16), 1058-1073, 2015 | 435 | 2015 |
| Unified representation of molecules and crystals for machine learning H Huo, M Rupp Machine Learning: Science and Technology 3 (4), 045017, 2022 | 384* | 2022 |
| DOGS: Reaction-Driven de novo Design of Bioactive Compounds M Hartenfeller, H Zettl, M Walter, M Rupp, F Reisen, E Proschak, ... PLoS computational biology 8 (2), e1002380, 2012 | 262 | 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 | 252 | 2015 |
| Machine Learning for Quantum Mechanical Properties of Atoms in Molecules M Rupp, R Ramakrishnan, OA von Lilienfeld Journal of Physical Chemistry Letters 6 (16), 3309-3313, 2015 | 250 | 2015 |
| Learning invariant representations of molecules for atomization energy prediction G Montavon, K Hansen, S Fazli, M Rupp, F Biegler, A Ziehe, ... Advances in neural information processing systems 25, 2012 | 205 | 2012 |
| Understanding machine‐learned density functionals L Li, JC Snyder, IM Pelaschier, J Huang, UN Niranjan, P Duncan, M Rupp, ... International Journal of Quantum Chemistry 116 (11), 819-833, 2016 | 204 | 2016 |
| Orbital-free bond breaking via machine learning JC Snyder, M Rupp, K Hansen, L Blooston, KR Müller, K Burke The Journal of chemical physics 139 (22), 2013 | 141 | 2013 |
| Identifying domains of applicability of machine learning models for materials science C Sutton, M Boley, LM Ghiringhelli, M Rupp, J Vreeken, M Scheffler Nature communications 11 (1), 4428, 2020 | 136 | 2020 |
| Machine-learned multi-system surrogate models for materials prediction N Chandramouli, M Rupp, B Brayden, AV Shapeev, T Mueller, ... npj Computational Materials 5 (1), 51, 2019 | 136* | 2019 |
| Understanding kernel ridge regression: Common behaviors from simple functions to density functionals K Vu, JC Snyder, L Li, M Rupp, BF Chen, T Khelif, KR Müller, K Burke International Journal of Quantum Chemistry 115 (16), 1115-1128, 2015 | 123 | 2015 |
| Optimizing transition states via kernel-based machine learning ZD Pozun, K Hansen, D Sheppard, M Rupp, KR Müller, G Henkelman The Journal of chemical physics 136 (17), 2012 | 122 | 2012 |
| Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning MF Langer, A Goeßmann, M Rupp npj Computational Materials 8 (1), 41, 2022 | 112 | 2022 |
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, Germany在 tu-berlin.de 的电子邮件经过验证