Alexandre Tkatchenko

Research.com Recognitions

• 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

Overview

Best Publications

• Accurate molecular van der Waals interactions from ground-state electron density and free-atom reference data

  Alexandre Tkatchenko;Matthias Scheffler

  6641
  Citations

• 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

  2487
  Citations

• SchNet - A deep learning architecture for molecules and materials.

  Kristof T. Schütt;Huziel E. Sauceda;P. J. Kindermans;Alexandre Tkatchenko

  2032
  Citations

• Accurate and Efficient Method for Many-Body van der Waals Interactions

  Alexandre Tkatchenko;Robert A. DiStasio;Roberto Car;Matthias Scheffler

  1687
  Citations

• Reproducibility in density functional theory calculations of solids

  Kurt Lejaeghere;Gustav Bihlmayer;Torbjörn Björkman;Torbjörn Björkman;Peter Blaha

  1523
  Citations

• Quantum-chemical insights from deep tensor neural networks.

  Kristof T. Schütt;Farhad Arbabzadah;Stefan Chmiela;Klaus R. Müller;Klaus R. Müller

  1393
  Citations

• Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package

  Evgeny Epifanovsky;Andrew T.B. Gilbert;Andrew T.B. Gilbert;Xintian Feng;Xintian Feng;Joonho Lee

  1230
  Citations

• Machine learning of accurate energy-conserving molecular force fields

  Stefan Chmiela;Alexandre Tkatchenko;Alexandre Tkatchenko;Huziel E. Sauceda;Igor Poltavsky

  1080
  Citations

• DFTB+, a software package for efficient approximate density functional theory based atomistic simulations

  B. Hourahine;B. Aradi;V. Blum;F. Bonafé

  1077
  Citations

• Machine Learning for Molecular Simulation.

  Frank Noé;Frank Noé;Alexandre Tkatchenko;Klaus Robert Müller;Klaus Robert Müller;Klaus Robert Müller;Cecilia Clementi;Cecilia Clementi

  1035
  Citations

• Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

  Katja Hansen;Franziska Biegler;Raghunathan Ramakrishnan;Wiktor Pronobis

  879
  Citations

• Machine Learning Force Fields

  Oliver T. Unke;Stefan Chmiela;Huziel E. Sauceda;Michael Gastegger

  841
  Citations

• 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

  805
  Citations

• Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

  Katja Hansen;Grégoire Montavon;Franziska Biegler;Siamac Fazli

  686
  Citations

• Long-range correlation energy calculated from coupled atomic response functions.

  Alberto Ambrosetti;Anthony M. Reilly;Robert A. DiStasio;Alexandre Tkatchenko

  667
  Citations

• Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields

  Stefan Chmiela;Huziel Sauceda;Klaus-Robert Müller;Alexandre Tkatchenko

  661
  Citations

• Report on the sixth blind test of organic crystal-structure prediction methods

  Anthony M. Reilly;Richard I. Cooper;Claire S. Adjiman;Saswata Bhattacharya

  644
  Citations

• 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

  642
  Citations

• Resolution-of-identity approach to Hartree-Fock, hybrid density functionals, RPA, MP2, and extit{GW} with numeric atom-centered orbital basis functions

  Xinguo Ren;Patrick Rinke;Volker Blum;Jürgen Wieferink

  561
  Citations

• Machine Learning of Molecular Electronic Properties in Chemical Compound Space

  Grégoire Montavon;Matthias Rupp;Vivekanand Gobre;Alvaro Vazquez-Mayagoitia

  545
  Citations