World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
69
Citations
20609
World Ranking
1115
National Ranking
16

Michele Ceriotti publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Michele Ceriotti sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 231 publications — 59th percentile

59% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Michele Ceriotti D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Michele Ceriotti sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 69 D-Index — 89th percentile

89% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Michele Ceriotti is a researcher affiliated with the École Polytechnique Fédérale de Lausanne in Switzerland. Their academic work focuses primarily on Materials Science, with a significant number of publications in the subfield of Materials Chemistry. Additional areas of study include Computational Theory and Mathematics, Atomic and Molecular Physics and Optics, Biomedical Engineering, and Molecular Biology.

Their research topics cover a broad spectrum within the materials and computational sciences, including:

  • Machine Learning in Materials Science
  • Computational Drug Discovery Methods
  • X-ray Diffraction in Crystallography
  • Protein Structure and Dynamics
  • Advanced Chemical Physics Studies
  • Electron and X-Ray Spectroscopy Techniques
  • Advanced Materials Characterization Techniques

Michele Ceriotti has contributed to numerous scientific publications, appearing frequently in specific venues such as:

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

Their recent published works include:

  • "Gaussian Process Regression for Materials and Molecules," 2021, Chemical Reviews
  • "Origins of structural and electronic transitions in disordered silicon," 2021, Nature
  • "Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems," 2020, arXiv (Cornell University)
  • "Incompleteness of Atomic Structure Representations," 2020, Physical Review Letters
  • "Roadmap on Machine learning in electronic structure," 2022, Electronic Structure

Collaborations are an important aspect of their work, with frequent co-authors including:

  • Guillaume Fraux
  • Jigyasa Nigam
  • Sergey N. Pozdnyakov
  • Federico Grasselli
  • Filippo Bigi

Best Publications

  • Physics-Inspired Structural Representations for Molecules and Materials.

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

  • Comparing molecules and solids across structural and alchemical space.

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

  • Machine learning unifies the modeling of materials and molecules

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

  • Nuclear Quantum Effects in Water and Aqueous Systems: Experiment, Theory, and Current Challenges

    Michele Ceriotti;Wei Fang;Peter G. Kusalik;Ross H. McKenzie

  • Nuclear quantum effects enter the mainstream

    Thomas E. Markland;Michele Ceriotti

  • Efficient stochastic thermostatting of path integral molecular dynamics.

    Michele Ceriotti;Michele Parrinello;Thomas E. Markland;David E. Manolopoulos

  • i-PI 2.0: A universal force engine for advanced molecular simulations

    Venkat Kapil;Mariana Rossi;Ondrej Marsalek;Ondrej Marsalek;Riccardo Petraglia

  • Barely porous organic cages for hydrogen isotope separation.

    Ming Liu;Linda Zhang;Marc A. Little;Venkat Kapil

  • Simplifying the representation of complex free-energy landscapes using sketch-map

    Michele Ceriotti;Gareth A. Tribello;Michele Parrinello

  • Ab initio thermodynamics of liquid and solid water

    Bingqing Cheng;Edgar A Engel;Jörg Behler;Christoph Dellago

  • 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

  • Nuclear quantum effects in solids using a colored-noise thermostat.

    Michele Ceriotti;Giovanni Bussi;Michele Parrinello

  • Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials.

    Giulio Imbalzano;Andrea Anelli;Daniele Giofré;Sinja Klees

  • Colored-Noise Thermostats à la Carte

    Michele Ceriotti;Giovanni Bussi;Michele Parrinello

  • How to remove the spurious resonances from ring polymer molecular dynamics

    Mariana Rossi;Michele Ceriotti;David E. Manolopoulos

  • i-PI: A Python interface for ab initio path integral molecular dynamics simulations

    Michele Ceriotti;Joshua More;David E. Manolopoulos

  • Nuclear quantum effects and hydrogen bond fluctuations in water

    Michele Ceriotti;Jérôme Cuny;Michele Parrinello;David E. Manolopoulos

  • Transferable Machine-Learning Model of the Electron Density

    Andrea Grisafi;Alberto Fabrizio;Benjamin Meyer;David M. Wilkins

  • Chemical shifts in molecular solids by machine learning

    Federico M. Paruzzo;Albert Hofstetter;Félix Musil;Sandip De

  • Langevin Equation with Colored Noise for Constant-Temperature Molecular Dynamics Simulations

    Michele Ceriotti;Giovanni Bussi;Michele Parrinello

  • Ab initio thermodynamics of liquid and solid water: supplemental materials

    Bingqing Cheng;Edgar Engel;Jörg Behler;Christoph Dellago

Frequent Co-Authors

David E. Manolopoulos
David E. Manolopoulos University of Oxford
Gábor Csányi
Gábor Csányi University of Cambridge
Marco Bernasconi
Marco Bernasconi University of Milano-Bicocca
Chris J. Pickard
Chris J. Pickard University of Cambridge
Davide Donadio
Davide Donadio University of California, Davis
Clémence Corminboeuf
Clémence Corminboeuf École Polytechnique Fédérale de Lausanne
Luciano Colombo
Luciano Colombo University of Cagliari
Francesco Paesani
Francesco Paesani University of California, San Diego
Lyndon Emsley
Lyndon Emsley École Polytechnique Fédérale de Lausanne

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