O. Anatole von Lilienfeld

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Alexandre TkatchenkoProfessor of Physics, University of Luxembourg; Visiting Professor, TU Berlin; APS Fellow; FRSC確認したメールアドレス: uni.lu
Matthias RuppLuxembourg Institute of Science and Technology (LIST)確認したメールアドレス: list.lu
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, Germany確認したメールアドレス: tu-berlin.de
Raghunathan RamakrishnanTata Institute of Fundamental Research Hyderabad, India確認したメールアドレス: tifrh.res.in
Guido Falk von RudorffDepartment of Chemistry, University Kassel確認したメールアドレス: uni-kassel.de
Huang BingWuhan University確認したメールアドレス: whu.edu.cn
Felix Andreas FaberResearcher in Data Science and Modelling, AstraZeneca確認したメールアドレス: astrazeneca.com
Ursula RothlisbergerProfessor of Computational Chemistry and Biochemistry確認したメールアドレス: epfl.ch
Anders S. ChristensenQuantum Consulting by Christensen確認したメールアドレス: unibas.ch
Ivano TavernelliIBM Quantum, IBM Research - Zurich確認したメールアドレス: zurich.ibm.com
Stefan HeinenPostdoc, Empa, High Performance Ceramics Lab確認したメールアドレス: empa.ch
Pavlo O. DralProfessor, Xiamen University確認したメールアドレス: xmu.edu.cn
Daniel SebastianiProfessor of Theoretical Chemistry, University Halle, Germany確認したメールアドレス: chemie.uni-halle.de
Grégoire MontavonGuest professor, Freie Universität Berlin確認したメールアドレス: fu-berlin.de
Mark E TuckermanNew York University確認したメールアドレス: nyu.edu
Louis-Francois ArsenaultColumbia university確認したメールアドレス: columbia.edu
Dominik LemmDepartment of Physics, University of Vienna確認したメールアドレス: univie.ac.at
Alejandro Lopez-BezanillaStaff Scientist at Los Alamos Nat'l Lab確認したメールアドレス: lanl.gov
Mauricio D. Coutinho-NetoUniversidade Federal do ABC確認したメールアドレス: ufabc.edu.br
Rickard ArmientoLinköping University確認したメールアドレス: liu.se

フォロー

O. Anatole von Lilienfeld

Clark & CIFAR AI chair at University of Toronto/Vector Institute/Technical University Berlin

確認したメールアドレス: utoronto.ca - ホームページ

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


タイトル引用回数順公開年順タイトル順	引用先引用先	年
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	2551	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	2150	2014
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	905	2015
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	890	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	807	2013
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	733*	2017
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
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	663	2004
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	516	2015
Long range interactions in nanoscale science RH French, VA Parsegian, R Podgornik, RF Rajter, A Jagota, J Luo, ... Reviews of Modern Physics 82 (2), 1887-1944, 2010	490	2010
Machine Learning Energies of 2 Million Elpasolite Crystals FA Faber, A Lindmaa, OA Von Lilienfeld, R Armiento Physical review letters 117 (13), 135502, 2016	481	2016
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), 2018	433	2018
Exploring chemical compound space with quantum-based machine learning OA von Lilienfeld, KR Müller, A Tkatchenko Nature Reviews Chemistry 4 (7), 347-358, 2020	349	2020
Retrospective on a decade of machine learning for chemical discovery OA von Lilienfeld, K Burke Nature communications 11 (1), 4895, 2020	343	2020
FCHL revisited: Faster and more accurate quantum machine learning AS Christensen, LA Bratholm, FA Faber, O Anatole von Lilienfeld The Journal of chemical physics 152 (4), 2020	337	2020
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), 2016	335	2016
Electronic spectra from TDDFT and machine learning in chemical space R Ramakrishnan, M Hartmann, E Tapavicza, OA Von Lilienfeld The Journal of chemical physics 143 (8), 2015	315	2015
Quantum machine learning in chemical compound space OA Von Lilienfeld Angewandte Chemie International Edition 57 (16), 4164-4169, 2018	271	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	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

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