World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
14428
World Ranking
9495
National Ranking
4023

John E. Stone 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 John E. Stone 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: 88 publications — 5th percentile

5% of scientists in this discipline score the same or lower.

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

John E. Stone 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 John E. Stone 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: 39 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Overview

John E. Stone is affiliated with Nvidia in the United States and has contributed extensively to the field of biochemistry, genetics, and molecular biology. Their research spans multiple subfields, including molecular biology, ecology, biomedical engineering, modeling and simulation, and biophysics.

The primary areas of study for their work encompass topics such as protein structure and dynamics, bacteriophages and microbial interactions, lipid membrane structure and behavior, COVID-19 epidemiological studies, bacterial genetics and biotechnology, nanopore and nanochannel transport studies, and SARS-CoV-2 and COVID-19 research.

Frequent coauthors collaborating with John E. Stone include David J. Hardy, J. C. Phillips, Emad Tajkhorshid, Anda Trifan, and Alexander Brace. These collaborations have contributed to a range of publications across several scientific venues.

Key publication venues where Stone's research has been featured regularly include:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • The International Journal of High Performance Computing Applications
  • Computing in Science & Engineering
  • Biophysical Journal
  • The Journal of Chemical Physics

Among recent notable papers authored or coauthored by John E. Stone are:

  • Scalable molecular dynamics on CPU and GPU architectures with NAMD (2020) in The Journal of Chemical Physics
  • AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics (2021) in The International Journal of High Performance Computing Applications
  • #COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol (2022) in The International Journal of High Performance Computing Applications
  • VMD as a Platform for Interactive Small Molecule Preparation and Visualization in Quantum and Classical Simulations (2023) in Journal of Chemical Information and Modeling
  • AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics (2020) in bioRxiv (Cold Spring Harbor Laboratory)

Best Publications

  • Scalable molecular dynamics on CPU and GPU architectures with NAMD.

    James C. Phillips;David J. Hardy;Julio D.C. Maia;John E. Stone

  • GPU Computing

    J.D. Owens;M. Houston;D. Luebke;S. Green

  • OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems

    John E Stone;David Gohara;Guochun Shi

  • Accelerating molecular modeling applications with graphics processors

    John E. Stone;James C. Phillips;Peter L. Freddolino;David J. Hardy

  • GPU-accelerated molecular modeling coming of age

    John E. Stone;David J. Hardy;Ivan S. Ufimtsev;Klaus Schulten

  • An efficient library for parallel ray tracing and animation

    John Edward Stone

  • GPU clusters for high-performance computing

    Volodymyr V. Kindratenko;Jeremy J. Enos;Guochun Shi;Michael T. Showerman

  • Fast analysis of molecular dynamics trajectories with graphics processing units-Radial distribution function histogramming

    Benjamin G. Levine;John E. Stone;Axel Kohlmeyer

  • A system for interactive molecular dynamics simulation

    John E. Stone;Justin Gullingsrud;Klaus Schulten

  • An asymmetric distributed shared memory model for heterogeneous parallel systems

    Isaac Gelado;John E. Stone;Javier Cabezas;Sanjay Patel

  • NAMD goes quantum: an integrative suite for hybrid simulations.

    Marcelo C.R. Melo;Rafael C. Bernardi;Till Rudack;Till Rudack;Maximilian Scheurer;Maximilian Scheurer

  • Adapting a message-driven parallel application to GPU-accelerated clusters

    James C. Phillips;John E. Stone;Klaus Schulten

  • QwikMD — Integrative Molecular Dynamics Toolkit for Novices and Experts

    João V. Ribeiro;Rafael C. Bernardi;Till Rudack;John E. Stone

  • Using VMD: an introductory tutorial.

    Jen Hsin;Anton Arkhipov;Ying Yin;John E. Stone

  • Molecular dynamics-based refinement and validation for sub-5 Å cryo-electron microscopy maps

    Abhishek Singharoy;Ivan Teo;Ryan McGreevy;John E Stone

  • Multilevel summation of electrostatic potentials using graphics processing units

    David J. Hardy;John E. Stone;Klaus Schulten

  • GPU acceleration of cutoff pair potentials for molecular modeling applications

    Christopher I. Rodrigues;David J. Hardy;John E. Stone;Klaus Schulten

  • Lattice Microbes: high-performance stochastic simulation method for the reaction-diffusion master equation.

    Elijah Roberts;John E. Stone;Zaida Luthey-Schulten

  • AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

    Lorenzo Casalino;Abigail C. Dommer;Zied Gaieb;Emilia P. Barros

  • Fast Visualization of Gaussian Density Surfaces for Molecular Dynamics and Particle System Trajectories

    Michael Krone;John E. Stone;Thomas Ertl;Klaus Schulten

  • AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics

    Lorenzo Casalino;Abigail Dommer;Zied Gaieb;Emilia P Barros

Frequent Co-Authors

Klaus Schulten
Klaus Schulten University of Illinois at Urbana-Champaign
Zaida Luthey-Schulten
Zaida Luthey-Schulten University of Illinois at Urbana-Champaign
Wen-mei W. Hwu
Wen-mei W. Hwu University of Illinois at Urbana-Champaign
Emad Tajkhorshid
Emad Tajkhorshid University of Illinois at Urbana-Champaign
Christophe Chipot
Christophe Chipot University of Illinois at Urbana-Champaign
Aleksei Aksimentiev
Aleksei Aksimentiev University of Illinois at Urbana-Champaign
Shantenu Jha
Shantenu Jha Rutgers, The State University of New Jersey
C. Neil Hunter
C. Neil Hunter University of Sheffield
Anima Anandkumar
Anima Anandkumar Nvidia (United Kingdom)
Syma Khalid
Syma Khalid University of Oxford

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