O. Anatole von Lilienfeld

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Katkıda bulunan yazarlar

Alexandre TkatchenkoProfessor of Physics, University of Luxembourg; Visiting Professor, Berlin Big Data Centeruni.lu üzerinde doğrulanmış e-posta adresine sahip
Matthias RuppUniversity of Konstanzuni-konstanz.de üzerinde doğrulanmış e-posta adresine sahip
Klaus-Robert MüllerGoogle Brain (on leave of Professor for Machine Learning, TU Berlin & Korea University, Seoul)tu-berlin.de üzerinde doğrulanmış e-posta adresine sahip
Raghunathan RamakrishnanReader, TIFR Centre for Interdisciplinary Sciences, Hyderabad, Indiatifrh.res.in üzerinde doğrulanmış e-posta adresine sahip
Ursula RothlisbergerProfessor of Computational Chemistry and Biochemistryepfl.ch üzerinde doğrulanmış e-posta adresine sahip
Felix Andreas FaberSNSF early postdoc fellow at the University of Cambridgecam.ac.uk üzerinde doğrulanmış e-posta adresine sahip
Huang BingUniversity of Baselunibas.ch üzerinde doğrulanmış e-posta adresine sahip
ivano tavernelliIBM Research - Zurich (ita'at'zurich.ibm.com)zurich.ibm.com üzerinde doğrulanmış e-posta adresine sahip
Anders S. ChristensenUniversität Basel, Switzerlandunibas.ch üzerinde doğrulanmış e-posta adresine sahip
Pavlo O. DralAssociate Professor, Xiamen Universityxmu.edu.cn üzerinde doğrulanmış e-posta adresine sahip
Mauricio D. Coutinho NetoUniversidade Federal do ABCufabc.edu.br üzerinde doğrulanmış e-posta adresine sahip
Grégoire MontavonTechnische Universität Berlintu-berlin.de üzerinde doğrulanmış e-posta adresine sahip
Rickard ArmientoLinköping Universityliu.se üzerinde doğrulanmış e-posta adresine sahip
Alexander LindmaaRacah Institute of Physics, Hebrew University of Jerusalemmail.huji.ac.il üzerinde doğrulanmış e-posta adresine sahip
Guido Falk von RudorffDepartment of Chemistry, University Baselunibas.ch üzerinde doğrulanmış e-posta adresine sahip
Louis-Francois ArsenaultColumbia universitycolumbia.edu üzerinde doğrulanmış e-posta adresine sahip
Mark E TuckermanNew York Universitynyu.edu üzerinde doğrulanmış e-posta adresine sahip
Alejandro Lopez-BezanillaStaff Scientist at Los Alamos Nat'l Lablanl.gov üzerinde doğrulanmış e-posta adresine sahip
Angelos MichaelidesUniversity of Cambridgecam.ac.uk üzerinde doğrulanmış e-posta adresine sahip
Yasmine S. Al-HamdaniUniversity of Zurichchem.uzh.ch üzerinde doğrulanmış e-posta adresine sahip

O. Anatole von Lilienfeld

Professor of Computational Materials Discovery, Faculty of Physics, University of Vienna

univie.ac.at üzerinde doğrulanmış e-posta adresine sahip - Ana Sayfa

computational physics theoretical chemistry quantum machine learning density functional theory chemical space


Başlık Alıntılara göre sırala Yıla göre sırala Başlığa göre sırala	Alıntı yapanlar Alıntı yapanlar	Yıl
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	1094	2012
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	585	2004
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	491	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	432	2013
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	422	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	417	2013
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	352	2010
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	334*	2017
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	300	2015
Machine Learning Energies of 2 Million Elpasolite Crystals FA Faber, A Lindmaa, OA Von Lilienfeld, R Armiento Physical review letters 117 (13), 135502, 2016	231	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	216	2015
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	198	2010
Collective many-body van der Waals interactions in molecular systems RA DiStasio, OA von Lilienfeld, A Tkatchenko Proceedings of the National Academy of Sciences 109 (37), 14791-14795, 2012	189	2012
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	184	2016
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	177	2007
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	165	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	156	2018
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	145	2015
Variational optimization of effective atom centered potentials for molecular properties OA von Lilienfeld, I Tavernelli, U Rothlisberger, D Sebastiani The Journal of chemical physics 122 (1), 014113, 2005	123	2005
Variational particle number approach for rational compound design OA von Lilienfeld, RD Lins, U Rothlisberger Physical review letters 95 (15), 153002, 2005	122	2005

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