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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.

Papers
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Van der Waals interactions between organic adsorbates and at organic/inorganic interfaces

TL;DR: In this paper, the authors review the state-of-the-art of describing van der Waals interactions by density functional theory with respect to accuracy and practicability, and emphasize the crucial importance of a balanced description of both geometry and electronic structure in order to understand and model the properties of such systems.
Journal ArticleDOI

Understanding the role of vibrations, exact exchange, and many-body van der Waals interactions in the cohesive properties of molecular crystals

TL;DR: The results show that the approach employed here can reach the demanding accuracy of crystal-structure prediction and organic material design with minimal empiricism.
Proceedings Article

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

TL;DR: This work proposes to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid, and obtains a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles.
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Dispersion Interactions with Density-Functional Theory: Benchmarking Semiempirical and Interatomic Pairwise Corrected Density Functionals

TL;DR: It is found that the semiempirical kinetic-energy-density dependence introduced in the M06 functionals mimics some of the nonlocal correlation needed to describe dispersion, however, long-range contributions are still missing.
Posted Content

Machine Learning Force Fields

TL;DR: An overview of applications of ML-FFs and the chemical insights that can be obtained from them is given, and a step-by-step guide for constructing and testing them from scratch is given.