Alexandre Tkatchenko

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Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyE-mail confirmado em tu-berlin.de
Matthias SchefflerDirector of NOMAD Laboratory at FHI of Max-Planck-GesellschaftE-mail confirmado em fhi-berlin.mpg.de
O. Anatole von LilienfeldClark & CIFAR AI chair at University of Toronto/Vector Institute/Technical University BerlinE-mail confirmado em utoronto.ca
Stefan ChmielaTechnische Universität BerlinE-mail confirmado em chmiela.com
Robert A. DiStasio Jr.Associate Professor, Chemistry and Chemical Biology, Cornell UniversityE-mail confirmado em cornell.edu
Huziel E. SaucedaAssistant Professor, Instituto de Física, Universidad Nacional Autonoma de MexicoE-mail confirmado em fisica.unam.mx
Igor PoltavskyPostDocE-mail confirmado em uni.lu
Kristof T. SchüttPfizer, Machine Learning ResearchE-mail confirmado em pfizer.com
Wei LiuChangchun Institute of Applied Chemistry, Chinese Academy of SciencesE-mail confirmado em ciac.ac.cn
alberto ambrosettiUniversità degli Studi di PadovaE-mail confirmado em pd.infn.it
Matthias RuppLuxembourg Institute of Science and Technology (LIST)E-mail confirmado em list.lu
Jan HermannMicrosoft Research AI for ScienceE-mail confirmado em microsoft.com
Leeor KronikWeizmann Institute of ScienceE-mail confirmado em weizmann.ac.il
Reinhard J. MaurerProfessor, Department of Chemistry & Department of Physics, University of WarwickE-mail confirmado em warwick.ac.uk
Angelos MichaelidesUniversity of CambridgeE-mail confirmado em cam.ac.uk
Leonardo Medrano SandonasTechnische Universität DresdenE-mail confirmado em tu-dresden.de
Johannes HojaUniversity of GrazE-mail confirmado em uni-graz.at
Valentin Vassilev-GalindoIMDEA Materials InstituteE-mail confirmado em imdea.org
Noa MaromAssociate Professor, Materials Science and Engineering, Carnegie Mellon UniversityE-mail confirmado em andrew.cmu.edu
Dario AlfeProfessor of Physics at University College London and Universita' di Napoli Federico IIE-mail confirmado em ucl.ac.uk

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Alexandre Tkatchenko

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

E-mail confirmado em uni.lu - Página inicial

Intermolecular Interactions AI for Science Chemical Physics Materials Physics


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
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	6478	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	2552	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	2077	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	1669	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	1619	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	1573*	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	1450	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	1343	2017
Machine learning force fields OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schutt, ... Chemical Reviews 121 (16), 10142-10186, 2021	1172	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	977	2020
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	917	2021
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	899	2015
Machine learning for molecular simulation F Noé, A Tkatchenko, KR Müller, C Clementi Annual review of physical chemistry 71 (1), 361-390, 2020	834	2020
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	816	2013
Towards exact molecular dynamics simulations with machine-learned force fields S Chmiela, HE Sauceda, KR Müller, A Tkatchenko Nature communications 9 (1), 3887, 2018	814	2018
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	804	2012
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
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	705	2014
Combining machine learning and computational chemistry for predictive insights into chemical systems JA Keith, V Vassilev-Galindo, B Cheng, S Chmiela, M Gastegger, ... Chem. Rev. 121, 9816-9872, 2021	662	2021
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	649	2012

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