Matthias Rupp

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Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIVerified email at tu-berlin.de
O. Anatole von LilienfeldTechnical University Berlin, Machine Learning groupVerified email at tu-berlin.de
Alexandre TkatchenkoProfessor of Physics, University of Luxembourg; Visiting Professor, Berlin Big Data CenterVerified email at uni.lu
Gisbert SchneiderETH ZurichVerified email at ethz.ch
Raghunathan RamakrishnanTIFR HyderabadVerified email at tifrh.res.in
Proschak EJunior Professor in Drug Design Verified email at bioinformatik.uni-frankfurt.de
Kieron BurkeUC Irvine chemistry and physicsVerified email at uci.edu
John C. SnyderFormer Postdoc, Technische Universität Berlin / Max-Planck-Institut fürVerified email at uci.edu
Matthias SchefflerDirector at Fritz-Haber-Institut der Max-Planck-GesellschaftVerified email at fhi-berlin.mpg.de
Grégoire MontavonTechnische Universität BerlinVerified email at tu-berlin.de
Pavlo O. DralAssociate Professor, Xiamen UniversityVerified email at xmu.edu.cn
Siamac FazliNazarbayev UniversityVerified email at nu.edu.kz
Haoyan HuoDepartment of Material Science and Engineering, University of California at BerkeleyVerified email at berkeley.edu
Vivekanand Vaman GobreAssistant Professor at School of Chemical Sciences, Goa UniversityVerified email at unigoa.ac.in
Alvaro Vazquez-Mayagoitia (Álvaro Vá...Argonne National LaboratoryVerified email at alcf.anl.gov
Li Li (李力)GoogleVerified email at google.com
Igor V. TetkoBIGCHEM GmbH & Helmholtz MunichVerified email at helmholtz-munich.de
Conrad RosenbrockBrigham Young UniversityVerified email at byu.edu
Alexander ShapeevSkolkovo Institute of Science and TechnologyVerified email at shapeev.com
David WingateAssistant professor of Computer Science, Brigham Young UniversityVerified email at cs.byu.edu

Matthias Rupp

University of Konstanz

Verified email at uni-konstanz.de - Homepage

Machine Learning for Computational Physics and Chemistry


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	1593	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	902	2014
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	554	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	549	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	479	2015
Finding density functionals with machine learning JC Snyder, M Rupp, K Hansen, KR Müller, K Burke Physical review letters 108 (25), 253002, 2012	462	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 (6), 533-554, 2011	421	2011
Machine learning for quantum mechanics in a nutshell M Rupp International Journal of Quantum Chemistry 115 (16), 1058-1073, 2015	311	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	206	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	195	2015
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	192	2012
Unified representation for machine learning of molecules and crystals H Huo, M Rupp arXiv preprint arXiv:1704.06439 13754, 2017	180*	2017
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	155	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	144	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), 224104, 2013	99	2013
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), 174101, 2012	91	2012
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	83	2015
Kernel approach to molecular similarity based on iterative graph similarity M Rupp, E Proschak, G Schneider Journal of chemical information and modeling 47 (6), 2280-2286, 2007	83	2007
Guest editorial: Special topic on data-enabled theoretical chemistry M Rupp, OA Von Lilienfeld, K Burke The Journal of Chemical Physics 148 (24), 241401, 2018	74	2018
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	57*	2019

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