Alexandre Tkatchenko

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Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyEmail verificata su tu-berlin.de
Matthias SchefflerDirector of NOMAD Laboratory at FHI of Max-Planck-GesellschaftEmail verificata su fhi-berlin.mpg.de
O. Anatole von LilienfeldClark & CIFAR AI chair at University of Toronto/Vector Institute/Technical University BerlinEmail verificata su utoronto.ca
Robert A. DiStasio Jr.Associate Professor, Chemistry and Chemical Biology, Cornell UniversityEmail verificata su cornell.edu
Stefan ChmielaTechnische Universität BerlinEmail verificata su chmiela.com
Igor PoltavskyPostDocEmail verificata su uni.lu
Huziel E. SaucedaAssistant Professor, Instituto de Física, Universidad Nacional Autonoma de MexicoEmail verificata su fisica.unam.mx
Kristof T. SchüttPfizer, Machine Learning ResearchEmail verificata su pfizer.com
Wei LiuChangchun Institute of Applied Chemistry, Chinese Academy of SciencesEmail verificata su ciac.ac.cn
Matthias RuppLuxembourg Institute of Science and Technology (LIST)Email verificata su list.lu
alberto ambrosettiUniversità degli Studi di PadovaEmail verificata su pd.infn.it
Jan HermannMicrosoft Research AI for ScienceEmail verificata su microsoft.com
Leeor KronikWeizmann Institute of ScienceEmail verificata su weizmann.ac.il
Reinhard J. MaurerProfessor, Department of Chemistry & Department of Physics, University of WarwickEmail verificata su warwick.ac.uk
Angelos MichaelidesUniversity of CambridgeEmail verificata su cam.ac.uk
Johannes HojaUniversity of GrazEmail verificata su uni-graz.at
Valentin Vassilev-GalindoIMDEA Materials InstituteEmail verificata su imdea.org
Noa MaromAssociate Professor, Materials Science and Engineering, Carnegie Mellon UniversityEmail verificata su andrew.cmu.edu
Dario AlfeProfessor of Physics at University College London and Universita' di Napoli Federico IIEmail verificata su ucl.ac.uk
Leonardo Medrano SandonasTechnische Universität DresdenEmail verificata su tu-dresden.de

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Alexandre Tkatchenko

Professor of Physics, University of Luxembourg; Visiting Professor, TU Berlin; APS Fellow; FRSC

Email verificata su uni.lu - Home page

Intermolecular Interactions AI for Science Chemical Physics Materials Physics


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
Accurate molecular van der Waals interactions from ground-state electron density and free-atom reference data A Tkatchenko, M Scheffler Physical Review Letters 102 (7), 073005, 2009	6316	2009
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning M Rupp, A Tkatchenko, KR Müller, OA von Lilienfeld Physical Review Letters 108, 058301, 2012	2470	2012
Schnet–a deep learning architecture for molecules and materials KT Schütt, HE Sauceda, PJ Kindermans, A Tkatchenko, KR Müller The Journal of Chemical Physics 148 (24), 2018	1948	2018
Quantum-chemical insights from deep tensor neural networks KT Schütt, F Arbabzadah, S Chmiela, KR Müller, A Tkatchenko Nature communications 8 (1), 13890, 2017	1594	2017
Accurate and efficient method for many-body van der Waals interactions A Tkatchenko, RA DiStasio Jr, R Car, M Scheffler Physical Review Letters 108 (23), 236402, 2012	1582	2012
Reproducibility in density functional theory calculations of solids K Lejaeghere, G Bihlmayer, T Björkman, P Blaha, S Blügel, V Blum, ... Science 351 (6280), aad3000, 2016	1493*	2016
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions K Schütt, PJ Kindermans, HE Sauceda Felix, S Chmiela, A Tkatchenko, ... Advances in neural information processing systems 30, 2017	1358	2017
Machine learning of accurate energy-conserving molecular force fields S Chmiela, A Tkatchenko, HE Sauceda, I Poltavsky, KT Schütt, KR Müller Science advances 3 (5), e1603015, 2017	1294	2017
Machine learning force fields OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ... Chemical Reviews 121 (16), 10142-10186, 2021	1046	2021
DFTB+, a software package for efficient approximate density functional theory based atomistic simulations B Hourahine, B Aradi, V Blum, F Bonafe, A Buccheri, C Camacho, ... The Journal of chemical physics 152 (12), 2020	909	2020
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	871	2015
Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package E Epifanovsky, ATB Gilbert, X Feng, J Lee, Y Mao, N Mardirossian, ... The Journal of chemical physics 155 (8), 2021	838	2021
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	793	2013
Resolution-of-identity approach to Hartree–Fock, hybrid density functionals, RPA, MP2 and GW with numeric atom-centered orbital basis functions X Ren, P Rinke, V Blum, J Wieferink, A Tkatchenko, A Sanfilippo, K Reuter, ... New Journal of Physics 14 (5), 053020, 2012	790	2012
Machine learning for molecular simulation F Noé, A Tkatchenko, KR Müller, C Clementi Annual review of physical chemistry 71 (1), 361-390, 2020	785	2020
Towards exact molecular dynamics simulations with machine-learned force fields S Chmiela, HE Sauceda, KR Müller, A Tkatchenko Nature communications 9 (1), 3887, 2018	765	2018
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	711	2013
Long-range correlation energy calculated from coupled atomic response functions A Ambrosetti, AM Reilly, RA DiStasio, A Tkatchenko The Journal of chemical physics 140 (18), 2014	675	2014
Density-functional theory with screened van der Waals interactions for the modeling of hybrid inorganic-organic systems VG Ruiz, W Liu, E Zojer, M Scheffler, A Tkatchenko Physical Review Letters 108 (14), 146103, 2012	646	2012
Report on the sixth blind test of organic crystal structure prediction methods AM Reilly, RI Cooper, CS Adjiman, S Bhattacharya, AD Boese, ... Acta Crystallographica Section B: Structural Science, Crystal Engineering …, 2016	624	2016

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