World's Best Scientists 2026 revealed!

D-Index & Metrics

Chemistry

D-Index
50
Citations
11189
World Ranking
14255
National Ranking
1038

Cecilia Clementi 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 Cecilia Clementi 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: 108 publications — 3rd percentile

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

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

Cecilia Clementi 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 Cecilia Clementi 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: 50 D-Index — 21st percentile

21% of scientists in this discipline score the same or lower.

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

Overview

Cecilia Clementi is affiliated with Freie Universität Berlin in Germany. Their research spans topics in biochemistry, genetics, molecular biology, and materials science. Within these broad fields, their work focuses particularly on materials chemistry, molecular biology, atomic and molecular physics, optics, statistical and nonlinear physics, and computational theory and mathematics.

The research topics they have contributed to include:

  • Protein Structure and Dynamics
  • Machine Learning in Materials Science
  • Computational Drug Discovery Methods
  • Enzyme Structure and Function
  • Block Copolymer Self-Assembly
  • Model Reduction and Neural Networks
  • Vaccines and Immunoinformatics Approaches

Their recent publications illustrate an emphasis on molecular simulation, machine learning techniques, and coarse-graining approaches in molecular dynamics. Notable papers include:

  • Unsupervised Learning Methods for Molecular Simulation Data, 2021, Chemical Reviews
  • Data-driven approximation of the Koopman generator: Model reduction, system identification, and control, 2020, Physica D Nonlinear Phenomena
  • TorchMD: A Deep Learning Framework for Molecular Simulations, 2021, Journal of Chemical Theory and Computation
  • Coarse graining molecular dynamics with graph neural networks, 2020, Refubium (Universitätsbibliothek der Freien Universität Berlin)
  • Machine learning coarse-grained potentials of protein thermodynamics, 2023, Nature Communications

Frequent co-authors collaborating with Cecilia Clementi include:

  • Frank Noé
  • Yaoyi Chen
  • Andreas Krämer
  • Nicholas E. Charron
  • Félix Musil

The scientist's work has been published multiple times in prominent venues, demonstrating a consistent engagement with several key journals and repositories. These frequent publication venues include:

  • arXiv (Cornell University)
  • The Journal of Chemical Physics
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Nature Communications
  • Journal of Chemical Theory and Computation

Best Publications

  • Topological and energetic factors: what determines the structural details of the transition state ensemble and "en-route" intermediates for protein folding? An investigation for small globular proteins

    Cecilia Clementi;Hugh Nymeyer;José Nelson Onuchic

  • 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 of Coarse-Grained Molecular Dynamics Force Fields.

    Jiang Wang;Simon Olsson;Christoph Wehmeyer;Adrià Pérez

  • Coarse-grained models of protein folding: toy models or predictive tools?

    Cecilia Clementi

  • Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction

    Payel Das;Mark Moll;Hernán Stamati;Lydia E. Kavraki

  • Unsupervised Learning Methods for Molecular Simulation Data.

    Aldo Glielmo;Brooke E. Husic;Alex Rodriguez;Cecilia Clementi

  • AWSEM-MD: protein structure prediction using coarse-grained physical potentials and bioinformatically based local structure biasing

    Aram Davtyan;Nicholas P. Schafer;Weihua Zheng;Cecilia Clementi

  • Quantifying the roughness on the free energy landscape: entropic bottlenecks and protein folding rates.

    Leslie L Chavez;José N Onuchic;Cecilia Clementi

  • Kinetic Distance and Kinetic Maps from Molecular Dynamics Simulation

    Frank Noé;Cecilia Clementi

  • How native-state topology affects the folding of dihydrofolate reductase and interleukin-1beta.

    Cecilia Clementi;Patricia A. Jennings;José N. Onuchic

  • Discovering Mountain Passes via Torchlight: Methods for the Definition of Reaction Coordinates and Pathways in Complex Macromolecular Reactions

    Mary A. Rohrdanz;Wenwei Zheng;Cecilia Clementi

  • Topological and energetic factors: what determines the structural details of the transition state ensemble and "on-route" intermediates for protein folding? An investigation for small globular proteins

    Cecilia Clementi;Hugh Nymeyer;Jose' Nelson Onuchic

  • TorchMD: A Deep Learning Framework for Molecular Simulations.

    Stefan Doerr;Maciej Majewski;Adrià Pérez;Andreas Krämer

  • Interplay among tertiary contacts, secondary structure formation and side-chain packing in the protein folding mechanism: all-atom representation study of protein L.

    Cecilia Clementi;Angel E. Garcı́a;José N. Onuchic

  • The effects of nonnative interactions on protein folding rates: theory and simulation.

    Cecilia Clementi;Cecilia Clementi;Steven S. Plotkin

  • Collective variables for the study of long-time kinetics from molecular trajectories: theory and methods.

    Frank Noé;Cecilia Clementi

  • Dynamics of polymer translocation through nanopores: theory meets experiment.

    Silvina Matysiak;Alberto Montesi;Matteo Pasquali;Anatoly B. Kolomeisky

  • Coarse graining molecular dynamics with graph neural networks

    Brooke E Husic;Nicholas E Charron;Dominik Lemm;Jiang Wang

  • Adaptive resolution simulation of liquid water

    Matej Praprotnik;Silvina Matysiak;Luigi Delle Site;Kurt Kremer

  • From coarse-grain to all-atom: toward multiscale analysis of protein landscapes.

    Allison P. Heath;Lydia E. Kavraki;Cecilia Clementi;Cecilia Clementi

  • Balancing energy and entropy: A minimalist model for the characterization of protein folding landscapes

    Payel Das;Silvina Matysiak;Cecilia Clementi

Frequent Co-Authors

Lydia E. Kavraki
Lydia E. Kavraki Rice University
José N. Onuchic
José N. Onuchic Rice University
Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Shantenu Jha
Shantenu Jha Rutgers, The State University of New Jersey
Kurt Kremer
Kurt Kremer Max Planck Institute for Polymer Research
Peter G. Wolynes
Peter G. Wolynes Rice University
Charles A. Laughton
Charles A. Laughton University of Nottingham
Mauro Maggioni
Mauro Maggioni Johns Hopkins University
Paolo Carloni
Paolo Carloni Forschungszentrum Jülich
Alexandre Tkatchenko
Alexandre Tkatchenko University of Luxembourg

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