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Matthias Rupp

D-Index & Metrics

Computer Science

D-Index
30
Citations
10974
World Ranking
13817
National Ranking
16

Matthias Rupp publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Matthias Rupp sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 70 publications — 2nd percentile

2% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Matthias Rupp D-index placement in Computer Science in 2026

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

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 30 D-Index — 3rd percentile

3% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Matthias Rupp is affiliated with the Luxembourg Institute of Science and Technology in Luxembourg. Their research primarily focuses on Materials Science, with significant contributions in Materials Chemistry, Computational Theory and Mathematics, Atomic and Molecular Physics and Optics, Artificial Intelligence, and Electrical and Electronic Engineering.

Their work spans several key topics within these fields, including:

  • Machine Learning in Materials Science
  • Computational Drug Discovery Methods
  • X-ray Diffraction in Crystallography
  • Quantum, superfluid, helium dynamics
  • Electronic and Structural Properties of Oxides
  • Fuel Cells and Related Materials
  • Nuclear Materials and Properties

Matthias Rupp has contributed numerous publications to a variety of scientific venues. The most frequent publication outlets include:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • npj Computational Materials
  • The Journal of Chemical Physics
  • Physical Review B

Among the recent papers authored or co-authored by Matthias Rupp are:

  • "Unified representation of molecules and crystals for machine learning," 2022, Machine Learning Science and Technology
  • "Identifying domains of applicability of machine learning models for materials science," 2020, Nature Communications
  • "Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning," 2022, npj Computational Materials
  • "Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization," 2020, The Journal of Chemical Physics
  • "Ultra-fast interpretable machine-learning potentials," 2023, npj Computational Materials

Their research collaborations frequently involve:

  • Matthias Scheffler
  • Marcel F. Langer
  • Bastian Jäckl
  • Florian Knoop
  • Christian Carbogno

Best Publications

  • 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

  • Quantum chemistry structures and properties of 134 kilo molecules

    Raghunathan Ramakrishnan;Pavlo O. Dral;Pavlo O. Dral;Matthias Rupp;O. Anatole von Lilienfeld

  • Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.

    Raghunathan Ramakrishnan;Pavlo O. Dral;Pavlo O. Dral;Matthias Rupp;O. Anatole von Lilienfeld

  • Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

    Iurii Sushko;Sergii Novotarskyi;Robert Körner;Anil Kumar Pandey

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

    Katja Hansen;Grégoire Montavon;Franziska Biegler;Siamac Fazli

  • Finding density functionals with machine learning.

    John C. Snyder;Matthias Rupp;Katja Hansen;Klaus Robert Müller;Klaus Robert Müller

  • Machine Learning of Molecular Electronic Properties in Chemical Compound Space

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

  • Machine learning for quantum mechanics in a nutshell

    Matthias Rupp;Matthias Rupp

  • DOGS: reaction-driven de novo design of bioactive compounds

    Markus Hartenfeller;Heiko Zettl;Miriam Walter;Matthias Rupp

  • Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

    Matthias Rupp;Raghunathan Ramakrishnan;O. Anatole von Lilienfeld

  • Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

    O. Anatole von Lilienfeld;O. Anatole von Lilienfeld;Raghunathan Ramakrishnan;Matthias Rupp;Aaron Knoll

  • Understanding machine‐learned density functionals

    Li Li;John C. Snyder;John C. Snyder;Isabelle M. Pelaschier;Isabelle M. Pelaschier;Jessica Huang

  • Learning Invariant Representations of Molecules for Atomization Energy Prediction

    Grégoire Montavon;Katja Hansen;Siamac Fazli;Matthias Rupp

  • Machine-learned multi-system surrogate models for materials prediction

    Chandramouli Nyshadham;Matthias Rupp;Brayden Bekker;Alexander V. Shapeev

  • Orbital-free bond breaking via machine learning

    John C. Snyder;Matthias Rupp;Matthias Rupp;Katja Hansen;Leo Blooston

  • Identifying domains of applicability of machine learning models for materials science.

    Christopher A. Sutton;Mario Boley;Luca M. Ghiringhelli;Matthias Rupp;Matthias Rupp

  • Unified Representation of Molecules and Crystals for Machine Learning

    Haoyan Huo;Matthias Rupp

  • Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

    Marcel Florin Langer;Alex Goeßmann;Matthias Rupp

  • Optimizing transition states via kernel-based machine learning

    Zachary D. Pozun;Zachary D. Pozun;Katja Hansen;Katja Hansen;Daniel Sheppard;Daniel Sheppard;Matthias Rupp;Matthias Rupp

  • Understanding kernel ridge regression: Common behaviors from simple functions to density functionals

    Kevin Vu;John C. Snyder;John C. Snyder;Li Li;Matthias Rupp

  • Kernel approach to molecular similarity based on iterative graph similarity.

    Matthias Rupp;Ewgenij Proschak;Gisbert Schneider

  • Machine Learning of Molecular Electronic Properties in Chemical Compound Space

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

  • Guest Editorial: Special Topic on Data-enabled Theoretical Chemistry

    Matthias Rupp;O. Anatole von Lilienfeld;Kieron Burke

  • Big Data meets Quantum Chemistry Approximations: The $\Delta$-Machine Learning Approach

    Raghunathan Ramakrishnan;Pavlo O. Dral;Matthias Rupp;O. Anatole von Lilienfeld

  • Finding density functionals with machine learning

    John Snyder;Matthias Rupp;Katja Hansen;Klaus Mueller

Frequent Co-Authors

Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
O. Anatole von Lilienfeld
O. Anatole von Lilienfeld University of Toronto
Gisbert Schneider
Gisbert Schneider ETH Zurich
Alexandre Tkatchenko
Alexandre Tkatchenko University of Luxembourg
Kieron Burke
Kieron Burke University of California, Irvine
Matthias Scheffler
Matthias Scheffler Fritz Haber Institute of the Max Planck Society
Igor V. Tetko
Igor V. Tetko Helmholtz Zentrum München
Grégoire Montavon
Grégoire Montavon Freie Universität Berlin
Gábor Csányi
Gábor Csányi University of Cambridge
Antony J. Williams
Antony J. Williams Environmental Protection Agency

External Links

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Best Scientists Citing Matthias Rupp

Klaus-Robert Müller

Klaus-Robert Müller

Technical University of Berlin

Publications: 109

O. Anatole von Lilienfeld

O. Anatole von Lilienfeld

University of Toronto

Publications: 81

Gisbert Schneider

Gisbert Schneider

ETH Zurich

Publications: 66

Igor V. Tetko

Igor V. Tetko

Helmholtz Zentrum München

Publications: 52

Michele Ceriotti

Michele Ceriotti

École Polytechnique Fédérale de Lausanne

Publications: 51

Alexandre Tkatchenko

Alexandre Tkatchenko

University of Luxembourg

Publications: 49

Alán Aspuru-Guzik

Alán Aspuru-Guzik

University of Toronto

Publications: 48

Gábor Csányi

Gábor Csányi

University of Cambridge

Publications: 38

Rampi Ramprasad

Rampi Ramprasad

Georgia Institute of Technology

Publications: 25

Ian Foster

Ian Foster

University of Chicago

Publications: 24

Karsten Reuter

Karsten Reuter

Fritz Haber Institute of the Max Planck Society

Publications: 22

Kieron Burke

Kieron Burke

University of California, Irvine

Publications: 21

Heather J. Kulik

Heather J. Kulik

MIT

Publications: 20

Richard M. Martin

Richard M. Martin

University of Illinois at Urbana-Champaign

Publications: 19

Markus Meuwly

Markus Meuwly

University of Basel

Publications: 19

Olexandr Isayev

Olexandr Isayev

Carnegie Mellon University

Publications: 18

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