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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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Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature
Huziel E. Sauceda,Huziel E. Sauceda,Valentin Vassilev-Galindo,Stefan Chmiela,Klaus-Robert Müller,Alexandre Tkatchenko +5 more
TL;DR: In this paper, the authors use machine learned force fields trained on coupled cluster reference data to show the dynamical strengthening of covalent and non-covalent molecular interactions induced by NQE.
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From quantum to continuum mechanics in the delamination of atomically-thin layers from substrates.
Paul Hauseux,Thanh Tung Nguyen,Alberto Ambrosetti,Katerine Saleme Ruiz,Stéphane Bordas,Stéphane Bordas,Alexandre Tkatchenko +6 more
TL;DR: The wavelike atomic deformation is shown as the origin for the observed ultra long-range stress in delamination of graphene from various substrates in an analytical and numerical variational approach that combines continuum mechanics and elasticity with quantum many-body treatment of van der Waals dispersion interactions.
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Charge transfer and adsorption energies in the iodine–Pt(1 1 1) interaction
TL;DR: In this article, the adsorption energies for iodine atom on the fcc, hcp, bridge, and atop sites of the Pt(1 1 1) surface were determined using ab initio DFT method in two different unit cells.
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Quantum Mechanics of Proteins in Explicit Water: The Role of Plasmon-Like Solute-Solvent Interactions
TL;DR: Observations arise from the highly delocalized and collective character of the interactions, suggesting a remarkable persistence of electron correlation through aqueous environments and providing the basis for long-range interaction mechanisms in biomolecular systems.
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Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields.
TL;DR: This analysis enables us to modify the generalized AMBER force field by reparametrizing short-range and bonded interactions with more expressive terms to make them more accurate, without sacrificing the key properties that make MM-FFs so successful.