World's Best Scientists 2026 revealed!
Alexandre Tkatchenko

Alexandre Tkatchenko

D-Index & Metrics

Physics

D-Index
94
Citations
49036
World Ranking
1958
National Ranking
1

Alexandre Tkatchenko publication distribution in Physics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Physics in 2026. The highlighted bar marks where Alexandre Tkatchenko sits on this spectrum.

103–122 publications: 7 scientists 123–142 publications: 10 scientists 143–162 publications: 31 scientists 163–182 publications: 35 scientists 183–202 publications: 53 scientists 203–222 publications: 81 scientists 223–242 publications: 85 scientists 243–262 publications: 112 scientists 263–282 publications: 114 scientists 283–302 publications: 126 scientists 303–322 publications: 136 scientists 323–342 publications: 162 scientists 343–362 publications: 155 scientists 363–382 publications: 156 scientists 383–402 publications: 134 scientists 403–422 publications: 145 scientists 423–442 publications: 147 scientists 443–462 publications: 131 scientists 463–482 publications: 131 scientists 483–502 publications: 116 scientists 503–522 publications: 106 scientists 523–542 publications: 103 scientists 543–562 publications: 87 scientists 563–582 publications: 84 scientists 583–602 publications: 94 scientists 603–622 publications: 70 scientists 623–642 publications: 76 scientists 643–662 publications: 60 scientists 663–682 publications: 54 scientists 683–702 publications: 60 scientists 703–722 publications: 48 scientists 723–742 publications: 64 scientists 743–762 publications: 48 scientists 763–782 publications: 37 scientists 783–802 publications: 43 scientists 803–822 publications: 34 scientists 823–842 publications: 36 scientists 843–862 publications: 32 scientists 863–882 publications: 32 scientists 883–902 publications: 31 scientists 903–922 publications: 25 scientists 923–942 publications: 15 scientists 943–962 publications: 24 scientists 963–982 publications: 13 scientists 983–1,002 publications: 21 scientists 1,003–1,022 publications: 21 scientists 1,023–1,042 publications: 16 scientists 1,043–1,062 publications: 9 scientists 1,063–1,082 publications: 19 scientists 1,083–1,102 publications: 11 scientists 1,103–1,122 publications: 17 scientists 1,123–1,142 publications: 11 scientists 1,143–1,162 publications: 7 scientists 1,163–1,182 publications: 4 scientists 1,183–1,202 publications: 10 scientists 1,203–1,222 publications: 8 scientists 1,223–1,242 publications: 16 scientists 1,243–1,262 publications: 4 scientists 1,263–1,282 publications: 10 scientists 1,283–1,302 publications: 5 scientists 1,303–1,322 publications: 7 scientists 1,323–1,342 publications: 4 scientists 1,343–1,362 publications: 8 scientists 1,363–1,382 publications: 6 scientists 1,383–1,402 publications: 7 scientists 1,403–1,422 publications: 3 scientists 1,423–1,442 publications: 4 scientists 1,443–1,462 publications: 4 scientists 1,463–1,468 publications: 3 scientists 1,469+ publications: 100 scientists
103 publications 1,469+

This scientist: 341 publications — 26th percentile

26% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,469 publications or more.

Alexandre Tkatchenko D-index placement in Physics in 2026

The chart shows the D-index (discipline H-index) distribution of Physics scientists ranked by Research.com in 2026. The highlighted bar marks where Alexandre Tkatchenko sits on this spectrum.

70–71 D-Index: 76 scientists 72–73 D-Index: 110 scientists 74–75 D-Index: 143 scientists 76–77 D-Index: 153 scientists 78–79 D-Index: 144 scientists 80–81 D-Index: 167 scientists 82–83 D-Index: 167 scientists 84–85 D-Index: 166 scientists 86–87 D-Index: 132 scientists 88–89 D-Index: 155 scientists 90–91 D-Index: 134 scientists 92–93 D-Index: 137 scientists 94–95 D-Index: 102 scientists 96–97 D-Index: 112 scientists 98–99 D-Index: 110 scientists 100–101 D-Index: 116 scientists 102–103 D-Index: 97 scientists 104–105 D-Index: 103 scientists 106–107 D-Index: 87 scientists 108–109 D-Index: 90 scientists 110–111 D-Index: 67 scientists 112–113 D-Index: 78 scientists 114–115 D-Index: 75 scientists 116–117 D-Index: 67 scientists 118–119 D-Index: 69 scientists 120–121 D-Index: 60 scientists 122–123 D-Index: 59 scientists 124–125 D-Index: 53 scientists 126–127 D-Index: 42 scientists 128–129 D-Index: 41 scientists 130–131 D-Index: 33 scientists 132–133 D-Index: 32 scientists 134–135 D-Index: 45 scientists 136–137 D-Index: 19 scientists 138–139 D-Index: 24 scientists 140–141 D-Index: 28 scientists 142–143 D-Index: 31 scientists 144–145 D-Index: 22 scientists 146–147 D-Index: 20 scientists 148–149 D-Index: 12 scientists 150–151 D-Index: 16 scientists 152–153 D-Index: 24 scientists 154–155 D-Index: 23 scientists 156–157 D-Index: 15 scientists 158–159 D-Index: 15 scientists 160–161 D-Index: 11 scientists 162–163 D-Index: 17 scientists 164–165 D-Index: 12 scientists 166–167 D-Index: 11 scientists 168–169 D-Index: 9 scientists 170–171 D-Index: 9 scientists 172–173 D-Index: 11 scientists 174–175 D-Index: 4 scientists 176–177 D-Index: 8 scientists 178–179 D-Index: 5 scientists 180–181 D-Index: 3 scientists 182–183 D-Index: 4 scientists 184 D-Index: 4 scientists 185+ D-Index: 99 scientists
70 D-Index 185+

This scientist: 94 D-Index — 47th percentile

47% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 185 D-Index or more.

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

Alexandre Tkatchenko is affiliated with the University of Luxembourg in Luxembourg. Their research primarily focuses on areas intersecting materials science and physics, with significant contributions to subfields such as materials chemistry, atomic and molecular physics and optics, computational theory and mathematics, molecular biology, and electrical and electronic engineering.

The scientist's body of work spans several key topics, notably:

  • Machine Learning in Materials Science
  • Computational Drug Discovery Methods
  • Advanced Chemical Physics Studies
  • Protein Structure and Dynamics
  • X-ray Diffraction in Crystallography
  • Crystallization and Solubility Studies
  • Spectroscopy and Quantum Chemical Studies

Alexandre Tkatchenko's recent publications include:

  • "Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems" (2021), published in Chemical Reviews
  • "Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems" (2020), published in arXiv (Cornell University)
  • "Machine learning for chemical discovery" (2020), published in Nature Communications
  • "Roadmap on Machine learning in electronic structure" (2022), published in Electronic Structure
  • "Density Functional Model for van der Waals Interactions: Unifying Many-Body Atomic Approaches with Nonlocal Functionals" (2020), published in Physical Review Letters

The scientist frequently collaborates with the following coauthors:

  • Matej Ditte
  • Leonardo Medrano Sandonas
  • Loïc Groslambert
  • Yann Cornaton
  • Patrick Pale

Frequent publication venues for their research comprise:

  • arXiv (Cornell University)
  • The Cambridge Structural Database
  • Nature Communications
  • Journal of Chemical Theory and Computation
  • The Journal of Chemical Physics

In addition to journal articles, Alexandre Tkatchenko has contributed to book publications, including a work published by Springer Science+Business Media titled Machine Learning Meets Quantum Physics (2020).

Among formal recognitions, Alexandre Tkatchenko was named a Fellow of the American Physical Society in 2019. The associated citation highlights the development of a novel framework for modeling and understanding van der Waals interactions in molecules and materials.

Best Publications

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

    Alexandre Tkatchenko;Matthias Scheffler

  • 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

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

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

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

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

  • Reproducibility in density functional theory calculations of solids

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

  • Quantum-chemical insights from deep tensor neural networks.

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

  • 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

  • Machine learning of accurate energy-conserving molecular force fields

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

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

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

  • Machine Learning for Molecular Simulation.

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

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

    Katja Hansen;Franziska Biegler;Raghunathan Ramakrishnan;Wiktor Pronobis

  • Machine Learning Force Fields

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

  • 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

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

    Katja Hansen;Grégoire Montavon;Franziska Biegler;Siamac Fazli

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

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

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

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

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

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

  • 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

  • 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

  • Machine Learning of Molecular Electronic Properties in Chemical Compound Space

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

Frequent Co-Authors

Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Matthias Scheffler
Matthias Scheffler Fritz Haber Institute of the Max Planck Society
Wei Liu
Wei Liu University of California, Riverside
O. Anatole von Lilienfeld
O. Anatole von Lilienfeld University of Toronto
Roberto Car
Roberto Car Princeton University
Angelos Michaelides
Angelos Michaelides University of Cambridge
Leeor Kronik
Leeor Kronik Weizmann Institute of Science
Matthias Rupp
Matthias Rupp Luxembourg Institute of Science and Technology
Karsten Reuter
Karsten Reuter Fritz Haber Institute of the Max Planck Society
Egbert Zojer
Egbert Zojer Graz University of Technology

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Best Scientists Citing Alexandre Tkatchenko

Trending Scientists

Recently Published Articles