Matthias Rupp

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Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
O. Anatole von LilienfeldClark & CIFAR AI chair at University of Toronto/Vector Institute/Technical University BerlinVerified email at utoronto.ca
Alexandre TkatchenkoProfessor of Physics, University of Luxembourg; Visiting Professor, TU Berlin; APS Fellow; FRSCVerified email at uni.lu
Raghunathan RamakrishnanTata Institute of Fundamental Research Hyderabad, IndiaVerified email at tifrh.res.in
Gisbert SchneiderETH ZurichVerified email at ethz.ch
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
Grégoire MontavonGuest professor, Freie Universität BerlinVerified email at fu-berlin.de
Pavlo O. DralProfessor, Xiamen UniversityVerified email at xmu.edu.cn
Proschak EJunior Professor in Drug Design Verified email at bioinformatik.uni-frankfurt.de
Matthias SchefflerDirector of NOMAD Laboratory at FHI of Max-Planck-GesellschaftVerified email at fhi-berlin.mpg.de
Siamac FazliNazarbayev UniversityVerified email at nu.edu.kz
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
Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of CambridgeVerified email at cam.ac.uk
Conrad RosenbrockBrigham Young UniversityVerified email at byu.edu
Alexander ShapeevSkolkovo Institute of Science and TechnologyVerified email at shapeev.com
David WingateAssociate professor of Computer Science, Brigham Young UniversityVerified email at cs.byu.edu
Marcel F. LangerEPFLVerified email at epfl.ch

Matthias Rupp

Luxembourg Institute of Science and Technology (LIST)

Verified email at list.lu - 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	2544	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
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
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
Finding density functionals with machine learning JC Snyder, M Rupp, K Hansen, KR Müller, K Burke Physical review letters 108 (25), 253002, 2012	690	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	676	2011
Machine learning for quantum mechanics in a nutshell M Rupp International Journal of Quantum Chemistry 115 (16), 1058-1073, 2015	448	2015
Unified representation of molecules and crystals for machine learning H Huo, M Rupp Machine Learning: Science and Technology 3 (4), 045017, 2022	400*	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	269	2012
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	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
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	213	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	208	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	144	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	143	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	142*	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	117	2022

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