World's Best Scientists 2026 revealed!

D-Index & Metrics

Chemistry

D-Index
72
Citations
28054
World Ranking
5151
National Ranking
300

Gábor Csányi publication distribution in Chemistry in 2026

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

61–80 publications: 66 scientists 81–100 publications: 302 scientists 101–120 publications: 623 scientists 121–140 publications: 918 scientists 141–160 publications: 1,218 scientists 161–180 publications: 1,350 scientists 181–200 publications: 1,344 scientists 201–220 publications: 1,281 scientists 221–240 publications: 1,216 scientists 241–260 publications: 1,100 scientists 261–280 publications: 979 scientists 281–300 publications: 939 scientists 301–320 publications: 764 scientists 321–340 publications: 643 scientists 341–360 publications: 628 scientists 361–380 publications: 522 scientists 381–400 publications: 459 scientists 401–420 publications: 397 scientists 421–440 publications: 327 scientists 441–460 publications: 270 scientists 461–480 publications: 265 scientists 481–500 publications: 252 scientists 501–520 publications: 201 scientists 521–540 publications: 185 scientists 541–560 publications: 148 scientists 561–580 publications: 148 scientists 581–600 publications: 132 scientists 601–620 publications: 114 scientists 621–640 publications: 104 scientists 641–660 publications: 91 scientists 661–680 publications: 92 scientists 681–700 publications: 73 scientists 701–720 publications: 57 scientists 721–740 publications: 54 scientists 741–760 publications: 67 scientists 761–780 publications: 45 scientists 781–800 publications: 46 scientists 801–820 publications: 39 scientists 821–840 publications: 32 scientists 841–860 publications: 36 scientists 861–880 publications: 29 scientists 881–900 publications: 26 scientists 901–920 publications: 24 scientists 921–940 publications: 14 scientists 941–960 publications: 23 scientists 961–980 publications: 28 scientists 981–1,000 publications: 15 scientists 1,001–1,020 publications: 29 scientists 1,021–1,040 publications: 12 scientists 1,041–1,060 publications: 19 scientists 1,061–1,080 publications: 12 scientists 1,081–1,100 publications: 6 scientists 1,101–1,120 publications: 8 scientists 1,121–1,140 publications: 12 scientists 1,141–1,160 publications: 5 scientists 1,161–1,180 publications: 6 scientists 1,181–1,200 publications: 14 scientists 1,201–1,220 publications: 7 scientists 1,221–1,240 publications: 2 scientists 1,241–1,260 publications: 6 scientists 1,261–1,280 publications: 4 scientists 1,281–1,294 publications: 6 scientists 1,295+ publications: 100 scientists
61 publications 1,295+

This scientist: 197 publications — 31st percentile

31% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,295 publications or more.

Gábor Csányi D-index placement in Chemistry in 2026

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

40–41 D-Index: 289 scientists 42–43 D-Index: 612 scientists 44–45 D-Index: 808 scientists 46–47 D-Index: 776 scientists 48–49 D-Index: 835 scientists 50–51 D-Index: 861 scientists 52–53 D-Index: 872 scientists 54–55 D-Index: 933 scientists 56–57 D-Index: 1,051 scientists 58–59 D-Index: 930 scientists 60–61 D-Index: 882 scientists 62–63 D-Index: 834 scientists 64–65 D-Index: 731 scientists 66–67 D-Index: 775 scientists 68–69 D-Index: 683 scientists 70–71 D-Index: 646 scientists 72–73 D-Index: 561 scientists 74–75 D-Index: 501 scientists 76–77 D-Index: 437 scientists 78–79 D-Index: 388 scientists 80–81 D-Index: 354 scientists 82–83 D-Index: 292 scientists 84–85 D-Index: 275 scientists 86–87 D-Index: 254 scientists 88–89 D-Index: 235 scientists 90–91 D-Index: 185 scientists 92–93 D-Index: 192 scientists 94–95 D-Index: 155 scientists 96–97 D-Index: 163 scientists 98–99 D-Index: 125 scientists 100–101 D-Index: 105 scientists 102–103 D-Index: 105 scientists 104–105 D-Index: 112 scientists 106–107 D-Index: 88 scientists 108–109 D-Index: 68 scientists 110–111 D-Index: 69 scientists 112–113 D-Index: 65 scientists 114–115 D-Index: 79 scientists 116–117 D-Index: 61 scientists 118–119 D-Index: 44 scientists 120–121 D-Index: 37 scientists 122–123 D-Index: 40 scientists 124–125 D-Index: 33 scientists 126–127 D-Index: 26 scientists 128–129 D-Index: 34 scientists 130–131 D-Index: 35 scientists 132–133 D-Index: 25 scientists 134–135 D-Index: 27 scientists 136–137 D-Index: 17 scientists 138–139 D-Index: 16 scientists 140–141 D-Index: 20 scientists 142–143 D-Index: 20 scientists 144–145 D-Index: 15 scientists 146–147 D-Index: 9 scientists 148–149 D-Index: 9 scientists 150–151 D-Index: 16 scientists 152–153 D-Index: 11 scientists 154–155 D-Index: 9 scientists 156–157 D-Index: 3 scientists 158 D-Index: 3 scientists 159+ D-Index: 98 scientists
40 D-Index 159+

This scientist: 72 D-Index — 71st percentile

71% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 159 D-Index or more.

Overview

Gábor Csányi is affiliated with the University of Cambridge in the United Kingdom and works primarily in the field of Materials Science. Their research encompasses several focused subfields including Materials Chemistry, Computational Theory and Mathematics, Atomic and Molecular Physics and Optics, Molecular Biology, and Electrical and Electronic Engineering.

Their notable research topics include Machine Learning in Materials Science, Computational Drug Discovery Methods, X-ray Diffraction in Crystallography, Protein Structure and Dynamics, Advanced Chemical Physics Studies, Electronic and Structural Properties of Oxides, and Crystallography and Molecular Interactions.

The scientist has contributed extensively to academic literature with frequent publications in several venues. The key publication venues are:

  • arXiv (Cornell University)
  • The Journal of Chemical Physics
  • Zenodo (CERN European Organization for Nuclear Research)
  • npj Computational Materials
  • Journal of Chemical Theory and Computation

Selected recent papers include:

  • Gaussian Process Regression for Materials and Molecules, 2021, Chemical Reviews
  • Performance and Cost Assessment of Machine Learning Interatomic Potentials, 2020, The Journal of Physical Chemistry A
  • Origins of structural and electronic transitions in disordered silicon, 2021, Nature
  • An accurate and transferable machine learning potential for carbon, 2020, Oxford University Research Archive (University of Oxford)
  • Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon, 2021, npj Computational Materials

Frequent co-authors working alongside Gábor Csányi include Volker L. Deringer, Christoph Ortner, Noam Bernstein, Albert P. Bartók, and Michele Ceriotti.

Best Publications

  • Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.

    Albert P. Bartók;Mike C. Payne;Risi Kondor;Gábor Csányi

  • On representing chemical environments

    Albert P. Bartók;Risi Kondor;Gábor Csányi

  • Gaussian Process Regression for Materials and Molecules.

    Volker L. Deringer;Albert P. Bartók;Noam Bernstein;David M. Wilkins

  • Performance and Cost Assessment of Machine Learning Interatomic Potentials.

    Yunxing Zuo;Chi Chen;Xiangguo Li;Zhi Deng

  • Machine Learning Interatomic Potentials as Emerging Tools for Materials Science.

    Volker L. Deringer;Miguel A. Caro;Gábor Csányi

  • Physics-Inspired Structural Representations for Molecules and Materials.

    Felix Musil;Andrea Grisafi;Albert P. Bartók;Christoph Ortner

  • Gaussian approximation potentials: A brief tutorial introduction

    Albert P. Bartók;Gábor Csányi

  • Comparing molecules and solids across structural and alchemical space.

    Sandip De;Albert P. Bartók;Gábor Csányi;Michele Ceriotti

  • Reinforcement of single-walled carbon nanotube bundles by intertube bridging

    A. Kis;G. Csányi;J.-P. Salvetat;Thien-Nga Lee

  • Machine learning unifies the modeling of materials and molecules

    Albert P. Bartók;Sandip De;Carl Poelking;Noam Bernstein

  • Machine learning based interatomic potential for amorphous carbon

    Volker L. Deringer;Gábor Csányi

  • Edge-functionalized and substitutionally doped graphene nanoribbons: Electronic and spin properties

    F. Cervantes-Sodi;G. Csányi;S. Piscanec;A. C. Ferrari

  • Surface diffusion: the low activation energy path for nanotube growth.

    S. Hofmann;G. Csányi;A. C. Ferrari;M. C. Payne

  • Modeling Molecular Interactions in Water: From Pairwise to Many-Body Potential Energy Functions.

    Gerardo Andrés Cisneros;Kjartan Thor Wikfeldt;Lars Ojamäe;Jibao Lu

  • An accurate and transferable machine learning potential for carbon.

    Patrick Rowe;Volker L Deringer;Piero Gasparotto;Gábor Csányi

  • The role of the interlayer state in the electronic structure of superconducting graphite intercalated compounds

    Gábor Csányi;P. B. Littlewood;Andriy H. Nevidomskyy;Chris J. Pickard

  • "Learn on the fly": a hybrid classical and quantum-mechanical molecular dynamics simulation.

    Gabor Csányi;T. Albaret;M. C. Payne;A. De Vita;A. De Vita

  • Accuracy and transferability of Gaussian approximation potential models for tungsten

    Wojciech J. Szlachta;Albert P. Bartók;Gábor Csányi

  • Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

    Andrea Grisafi;David M. Wilkins;Gábor Csányi;Michele Ceriotti

  • Origins of structural and electronic transitions in disordered silicon

    Volker L. Deringer;Noam Bernstein;Gábor Csányi;Chiheb Ben Mahmoud

  • Chemically active substitutional nitrogen impurity in carbon nanotubes.

    Andriy H. Nevidomskyy;Gábor Csányi;Michael C. Payne

  • Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties

    Olga Obrezanova;Gábor Csányi;Joelle M. R. Gola;Matthew D. Segall

Frequent Co-Authors

Mike C. Payne
Mike C. Payne University of Cambridge
Christoph Ortner
Christoph Ortner University of Warwick
Michele Ceriotti
Michele Ceriotti École Polytechnique Fédérale de Lausanne
Stephen R. Elliott
Stephen R. Elliott University of Oxford
Chris J. Pickard
Chris J. Pickard University of Cambridge
Andrea C. Ferrari
Andrea C. Ferrari University of Cambridge
Karsten Reuter
Karsten Reuter Fritz Haber Institute of the Max Planck Society
Davide M. Proserpio
Davide M. Proserpio University of Milan
Stephan Hofmann
Stephan Hofmann University of Cambridge
Michael Moseler
Michael Moseler University of Freiburg

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