Matthias Rupp
Cited by
Cited by
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
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
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
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
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
Finding density functionals with machine learning
JC Snyder, M Rupp, K Hansen, KR Müller, K Burke
Physical review letters 108 (25), 253002, 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
Machine learning for quantum mechanics in a nutshell
M Rupp
International Journal of Quantum Chemistry 115 (16), 1058-1073, 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
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
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
Unified representation for machine learning of molecules and crystals
H Huo, M Rupp
arXiv preprint arXiv:1704.06439 13754, 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, 440-448, 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
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
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
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
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
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
Visual Interpretation of Kernel‐based prediction models
K Hansen, D Baehrens, T Schroeter, M Rupp, KR Müller
Molecular Informatics 30 (9), 817-826, 2011
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Articles 1–20