2019 - Fellow of American Physical Society (APS) Citation For the development of a novel framework for modeling and understanding van der Waals interactions in molecules and materials
His main research concerns van der Waals force, Density functional theory, Artificial intelligence, Statistical physics and Molecule. Alexandre Tkatchenko has researched van der Waals force in several fields, including Chemical physics, Nanotechnology and Thermodynamics. His work in the fields of Basis set overlaps with other areas such as Nitride.
Alexandre Tkatchenko interconnects Machine learning and Observable in the investigation of issues within Artificial intelligence. In his study, which falls under the umbrella issue of Statistical physics, Polarizability is strongly linked to Electronic structure. The concepts of his Molecule study are interwoven with issues in Electron and Chemisorption.
Alexandre Tkatchenko mainly focuses on van der Waals force, Chemical physics, Density functional theory, Molecule and Artificial intelligence. His van der Waals force research integrates issues from Computational chemistry and Condensed matter physics. His work focuses on many connections between Chemical physics and other disciplines, such as Electronic structure, that overlap with his field of interest in Ab initio.
His Density functional theory research focuses on Binding energy and how it connects with Non-covalent interactions. In his work, Metal is strongly intertwined with Adsorption, which is a subfield of Molecule. His work in Artificial intelligence addresses subjects such as Machine learning, which are connected to disciplines such as Potential energy.
Alexandre Tkatchenko spends much of his time researching Artificial intelligence, van der Waals force, Machine learning, Molecular dynamics and Quantum. He studies Artificial intelligence, focusing on Artificial neural network in particular. Van der Waals force is a subfield of Molecule that he tackles.
His Machine learning research includes elements of Complex system, Potential energy and Observable. His Quantum study combines topics in areas such as Conservative force, Electric field and Density functional theory. In general Density functional theory, his work in Hybrid functional is often linked to Path linking many areas of study.
Alexandre Tkatchenko mostly deals with Artificial intelligence, Machine learning, Molecular dynamics, Polarizability and van der Waals force. His work on Artificial neural network is typically connected to Permission as part of general Artificial intelligence study, connecting several disciplines of science. His Machine learning research incorporates elements of Quantum, Force field, Molecular mechanics and Observable.
His Molecular dynamics study combines topics from a wide range of disciplines, such as Statistical physics and Density functional theory. His biological study spans a wide range of topics, including Parallel algorithm, Electronic structure, Proton and Computational science. Alexandre Tkatchenko undertakes interdisciplinary study in the fields of van der Waals force and Radius through his works.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Accurate molecular van der Waals interactions from ground-state electron density and free-atom reference data
Alexandre Tkatchenko;Matthias Scheffler.
Physical Review Letters (2009)
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.
Physical Review Letters (2012)
Accurate and Efficient Method for Many-Body van der Waals Interactions
Alexandre Tkatchenko;Robert A. DiStasio;Roberto Car;Matthias Scheffler.
Physical Review Letters (2012)
Quantum-chemical insights from deep tensor neural networks.
Kristof T. Schütt;Farhad Arbabzadah;Stefan Chmiela;Klaus R. Müller;Klaus R. Müller.
Nature Communications (2017)
Reproducibility in density functional theory calculations of solids
Kurt Lejaeghere;Gustav Bihlmayer;Torbjörn Björkman;Torbjörn Björkman;Peter Blaha.
Science (2016)
Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela;Alexandre Tkatchenko;Alexandre Tkatchenko;Huziel E. Sauceda;Igor Poltavsky.
Science Advances (2017)
SchNet - A deep learning architecture for molecules and materials.
Kristof T. Schütt;Huziel E. Sauceda;P. J. Kindermans;Alexandre Tkatchenko.
Journal of Chemical Physics (2018)
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
Katja Hansen;Franziska Biegler;Raghunathan Ramakrishnan;Wiktor Pronobis.
Journal of Physical Chemistry Letters (2015)
Density-Functional Theory with Screened van der Waals Interactions for the Modeling of Hybrid Inorganic-Organic Systems
Victor G. Ruiz;Wei Liu;Egbert Zojer;Matthias Scheffler.
Physical Review Letters (2012)
Resolution-of-identity approach to Hartree?Fock, hybrid density functionals, RPA, MP2 and GW with numeric atom-centered orbital basis functions
Xinguo Ren;Patrick Rinke;Volker Blum;Jürgen Wieferink.
New Journal of Physics (2012)
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