Alexandre Tkatchenko
Alexandre Tkatchenko
Professor of Physics, University of Luxembourg; Visiting Professor, TU Berlin; APS Fellow; FRSC
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Cited by
Cited by
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
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
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
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
Quantum-chemical insights from deep tensor neural networks
KT Schütt, F Arbabzadah, S Chmiela, KR Müller, A Tkatchenko
Nature communications 8 (1), 13890, 2017
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
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
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
Machine learning force fields
OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ...
Chemical Reviews 121 (16), 10142-10186, 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
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
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
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
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
Machine learning for molecular simulation
F Noé, A Tkatchenko, KR Müller, C Clementi
Annual review of physical chemistry 71 (1), 361-390, 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
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
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
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
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
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