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Alexandre Tkatchenko
Researcher at University of Luxembourg
Publications - 298
Citations - 36119
Alexandre Tkatchenko is an academic researcher from University of Luxembourg. The author has contributed to research in topics: van der Waals force & Density functional theory. The author has an hindex of 77, co-authored 271 publications receiving 26863 citations. Previous affiliations of Alexandre Tkatchenko include Max Planck Society & Rafael Advanced Defense Systems.
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Journal ArticleDOI
Accurate molecular van der Waals interactions from ground-state electron density and free-atom reference data
TL;DR: It is shown that the effective atomic C6 coefficients depend strongly on the bonding environment of an atom in a molecule, and the van der Waals radii and the damping function in the C6R(-6) correction method for density-functional theory calculations.
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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp,Matthias Rupp,Alexandre Tkatchenko,Alexandre Tkatchenko,Klaus-Robert Müller,Klaus-Robert Müller,O. Anatole von Lilienfeld,O. Anatole von Lilienfeld +7 more
TL;DR: A machine learning model is introduced to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only, and applicability is demonstrated for the prediction of molecular atomization potential energy curves.
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Accurate and Efficient Method for Many-Body van der Waals Interactions
TL;DR: It is shown that the screening and the many-body vdW energy play a significant role even for rather small molecules, becoming crucial for an accurate treatment of conformational energies for biomolecules and binding of molecular crystals.
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Reproducibility in density functional theory calculations of solids
Kurt Lejaeghere,Gustav Bihlmayer,Torbjörn Björkman,Torbjörn Björkman,Peter Blaha,Stefan Blügel,Volker Blum,Damien Caliste,Ivano E. Castelli,Stewart J. Clark,Andrea Dal Corso,Stefano de Gironcoli,Thierry Deutsch,J. K. Dewhurst,Igor Di Marco,Claudia Draxl,Claudia Draxl,Marcin Dulak,Olle Eriksson,José A. Flores-Livas,Kevin F. Garrity,Luigi Genovese,Paolo Giannozzi,Matteo Giantomassi,Stefan Goedecker,Xavier Gonze,Oscar Grånäs,Oscar Grånäs,E. K. U. Gross,Andris Gulans,Andris Gulans,Francois Gygi,D. R. Hamann,P. J. Hasnip,Natalie Holzwarth,Diana Iusan,Dominik B. Jochym,F. Jollet,Daniel M. Jones,Georg Kresse,Klaus Koepernik,Klaus Koepernik,Emine Kucukbenli,Emine Kucukbenli,Yaroslav Kvashnin,Inka L. M. Locht,Inka L. M. Locht,Sven Lubeck,Martijn Marsman,Nicola Marzari,Ulrike Nitzsche,Lars Nordström,Taisuke Ozaki,Lorenzo Paulatto,Chris J. Pickard,Ward Poelmans,Matt Probert,Keith Refson,Keith Refson,Manuel Richter,Manuel Richter,Gian-Marco Rignanese,Santanu Saha,Matthias Scheffler,Matthias Scheffler,Martin Schlipf,Karlheinz Schwarz,Sangeeta Sharma,Francesca Tavazza,Patrik Thunström,Alexandre Tkatchenko,Alexandre Tkatchenko,Marc Torrent,David Vanderbilt,Michiel van Setten,Veronique Van Speybroeck,John M. Wills,Jonathan R. Yates,Guo-Xu Zhang,Stefaan Cottenier +79 more
TL;DR: A procedure to assess the precision of DFT methods was devised and used to demonstrate reproducibility among many of the most widely used DFT codes, demonstrating that the precisionof DFT implementations can be determined, even in the absence of one absolute reference code.
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SchNet - A deep learning architecture for molecules and materials.
Kristof T. Schütt,Huziel E. Sauceda,Pieter-Jan Kindermans,Alexandre Tkatchenko,Klaus-Robert Müller +4 more
TL;DR: SchNet as mentioned in this paper is a deep learning architecture specifically designed to model atomistic systems by making use of continuous-filter convolutional layers, where the model learns chemically plausible embeddings of atom types across the periodic table.