World's Best Scientists 2026 revealed!

D-Index & Metrics

Materials Science

D-Index
82
Citations
30040
World Ranking
2439
National Ranking
703

Chemistry

D-Index
81
Citations
27499
World Ranking
3199
National Ranking
1063

Dane Morgan publication distribution in Materials Science in 2026

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

50–69 publications: 28 scientists 70–89 publications: 152 scientists 90–109 publications: 356 scientists 110–129 publications: 487 scientists 130–149 publications: 723 scientists 150–169 publications: 835 scientists 170–189 publications: 850 scientists 190–209 publications: 891 scientists 210–229 publications: 862 scientists 230–249 publications: 766 scientists 250–269 publications: 726 scientists 270–289 publications: 665 scientists 290–309 publications: 593 scientists 310–329 publications: 537 scientists 330–349 publications: 477 scientists 350–369 publications: 440 scientists 370–389 publications: 356 scientists 390–409 publications: 321 scientists 410–429 publications: 256 scientists 430–449 publications: 246 scientists 450–469 publications: 216 scientists 470–489 publications: 212 scientists 490–509 publications: 174 scientists 510–529 publications: 194 scientists 530–549 publications: 162 scientists 550–569 publications: 131 scientists 570–589 publications: 111 scientists 590–609 publications: 103 scientists 610–629 publications: 99 scientists 630–649 publications: 77 scientists 650–669 publications: 92 scientists 670–689 publications: 56 scientists 690–709 publications: 53 scientists 710–729 publications: 53 scientists 730–749 publications: 38 scientists 750–769 publications: 52 scientists 770–789 publications: 43 scientists 790–809 publications: 38 scientists 810–829 publications: 34 scientists 830–849 publications: 25 scientists 850–869 publications: 18 scientists 870–889 publications: 20 scientists 890–909 publications: 24 scientists 910–929 publications: 27 scientists 930–949 publications: 20 scientists 950–969 publications: 17 scientists 970–989 publications: 10 scientists 990–1,009 publications: 16 scientists 1,010–1,029 publications: 13 scientists 1,030–1,049 publications: 12 scientists 1,050–1,069 publications: 9 scientists 1,070–1,089 publications: 8 scientists 1,090–1,109 publications: 7 scientists 1,110–1,129 publications: 9 scientists 1,130–1,149 publications: 2 scientists 1,150–1,162 publications: 5 scientists 1,163+ publications: 100 scientists
50 publications 1,163+

This scientist: 502 publications — 86th percentile

86% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,163 publications or more.

Dane Morgan D-index placement in Materials Science in 2026

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

40–41 D-Index: 211 scientists 42–43 D-Index: 450 scientists 44–45 D-Index: 612 scientists 46–47 D-Index: 612 scientists 48–49 D-Index: 598 scientists 50–51 D-Index: 657 scientists 52–53 D-Index: 667 scientists 54–55 D-Index: 621 scientists 56–57 D-Index: 597 scientists 58–59 D-Index: 610 scientists 60–61 D-Index: 587 scientists 62–63 D-Index: 606 scientists 64–65 D-Index: 533 scientists 66–67 D-Index: 490 scientists 68–69 D-Index: 469 scientists 70–71 D-Index: 378 scientists 72–73 D-Index: 421 scientists 74–75 D-Index: 359 scientists 76–77 D-Index: 323 scientists 78–79 D-Index: 299 scientists 80–81 D-Index: 230 scientists 82–83 D-Index: 210 scientists 84–85 D-Index: 195 scientists 86–87 D-Index: 203 scientists 88–89 D-Index: 175 scientists 90–91 D-Index: 175 scientists 92–93 D-Index: 142 scientists 94–95 D-Index: 121 scientists 96–97 D-Index: 117 scientists 98–99 D-Index: 107 scientists 100–101 D-Index: 88 scientists 102–103 D-Index: 85 scientists 104–105 D-Index: 68 scientists 106–107 D-Index: 62 scientists 108–109 D-Index: 57 scientists 110–111 D-Index: 45 scientists 112–113 D-Index: 49 scientists 114–115 D-Index: 50 scientists 116–117 D-Index: 34 scientists 118–119 D-Index: 38 scientists 120–121 D-Index: 37 scientists 122–123 D-Index: 29 scientists 124–125 D-Index: 28 scientists 126–127 D-Index: 24 scientists 128–129 D-Index: 33 scientists 130–131 D-Index: 28 scientists 132–133 D-Index: 21 scientists 134–135 D-Index: 20 scientists 136–137 D-Index: 23 scientists 138–139 D-Index: 17 scientists 140–141 D-Index: 12 scientists 142–143 D-Index: 17 scientists 144–145 D-Index: 21 scientists 146–147 D-Index: 13 scientists 148–149 D-Index: 11 scientists 150–151 D-Index: 14 scientists 152–153 D-Index: 13 scientists 154–155 D-Index: 9 scientists 156–157 D-Index: 10 scientists 158–159 D-Index: 7 scientists 160–161 D-Index: 4 scientists 162–163 D-Index: 4 scientists 164 D-Index: 3 scientists 165+ D-Index: 98 scientists
40 D-Index 165+

This scientist: 82 D-Index — 81st percentile

81% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 165 D-Index or more.

Overview

Dane Morgan is affiliated with the University of Wisconsin-Madison in the United States. Their research spans multiple areas primarily within materials science and engineering, with a significant focus on the applications of machine learning in materials research.

The scientist's main fields of study include:

  • Materials Science
  • Engineering

Within these broader fields, their subfields of research extend into:

  • Materials Chemistry
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Surfaces, Coatings and Films
  • Biomedical Engineering

Their work addresses a range of topics, reflecting a specialized focus on materials and computational methods:

  • Machine Learning in Materials Science
  • Semiconductor materials and devices
  • Electron and X-Ray Spectroscopy Techniques
  • Electronic and Structural Properties of Oxides
  • Nuclear Materials and Properties
  • Advancements in Solid Oxide Fuel Cells
  • Material Dynamics and Properties

Their frequent publication venues illustrate their engagement with both computational methods and material characterization:

  • arXiv (Cornell University)
  • Microscopy and Microanalysis
  • Computational Materials Science
  • ECS Meeting Abstracts
  • Journal of Nuclear Materials

Among notable papers associated with the scientist, these represent a selection highlighting recent work and impact:

  • "Machine learning in nuclear materials research," 2022, Current Opinion in Solid State and Materials Science
  • "Extracting accurate materials data from research papers with conversational language models and prompt engineering," 2024, Nature Communications
  • "Electronic Structure-Based Descriptors for Oxide Properties and Functions," 2022, Accounts of Chemical Research
  • "Work Function: Fundamentals, Measurement, Calculation, Engineering, and Applications," 2023, Physical Review Applied
  • "Radiation-induced segregation in a ceramic," 2020, Nature Materials

The scientist collaborates extensively with colleagues, with frequent co-authorship from:

  • Ryan Jacobs
  • John H. Booske
  • Izabela Szlufarska
  • Paul M. Voyles
  • Maciej P. Polak

Best Publications

  • First-principles study of native point defects in ZnO

    A. F. Kohan;G. Ceder;D. Morgan;Chris G. Van de Walle

  • AFLOW: An automatic framework for high-throughput materials discovery

    Stefano Curtarolo;Wahyu Setyawan;Gus L Hart;Michal Jahnatek

  • LI CONDUCTIVITY IN LIX MPO 4 ( M = MN , FE , CO , NI ) OLIVINE MATERIALS

    D Morgan;A J Van Der Ven;G Ceder

  • Instability of Pt ∕ C Electrocatalysts in Proton Exchange Membrane Fuel Cells A Mechanistic Investigation

    P. J. Ferreira;Y. Shao-Horn;D. Morgan

  • First-principles prediction of redox potentials in transition-metal compounds with LDA+ U

    Fei Zhou;Matteo Cococcioni;Chris A. Marianetti;Dane Morgan

  • Instability of Supported Platinum Nanoparticles in Low-Temperature Fuel Cells

    Y. Shao-Horn;W. C. Sheng;S. Chen;Paulo J Ferreira

  • Prediction of solid oxide fuel cell cathode activity with first-principles descriptors

    Yueh-Lin Lee;Jesper Kleis;Jan Rossmeisl;Yang Shao-Horn

  • Ab initio energetics of LaBO3(001) (B=Mn, Fe, Co, and Ni) for solid oxide fuel cell cathodes

    Yueh-Lin Lee;Jesper Kleis;Jan Rossmeisl;Dane Morgan

  • Predicting crystal structure by merging data mining with quantum mechanics.

    Christopher C. Fischer;Kevin J. Tibbetts;Dane Morgan;Gerbrand Ceder

  • The electronic structure and band gap of LiFePO4 and LiMnPO4

    Fei Zhou;Kisuk Kang;Thomas Maxisch;Gerbrand Ceder

  • New frontiers for the materials genome initiative

    Juan J. de Pablo;Nicholas E. Jackson;Michael A. Webb;Long Qing Chen

  • Predicting crystal structures with data mining of quantum calculations.

    Stefano Curtarolo;Dane Morgan;Kristin Persson;John Rodgers

  • Accuracy of ab initio methods in predicting the crystal structures of metals: A review of 80 binary alloys

    Stefano Curtarolo;Dane Morgan;Gerbrand Ceder

  • Pt nanoparticle stability in PEM fuel cells: influence of particle size distribution and crossover hydrogen

    Edward F. Holby;Wenchao Sheng;Yang Shao-Horn;Dane Morgan

  • Opportunities and Challenges for Machine Learning in Materials Science

    Dane Morgan;Ryan Jacobs

  • Surface strontium enrichment on highly active perovskites for oxygen electrocatalysis in solid oxide fuel cells

    Ethan J. Crumlin;Eva Mutoro;Zhi Liu;Michael E. Grass

  • Nondilute diffusion from first principles: Li diffusion in Li x TiS 2

    Anton Van der Ven;John C. Thomas;Qingchuan Xu;Benjamin Swoboda

  • Electrochemical modeling of intercalation processes with phase field models

    B.C. Han;A. Van der Ven;D. Morgan;G. Ceder

  • First-principles study of the stability and electronic structure of metal hydrides

    H. Smithson;H. Smithson;C. A. Marianetti;D. Morgan;A. Van der Ven

  • Phase Stability of Nickel Hydroxides and Oxyhydroxides

    A. Van der Ven;D. Morgan;Y. S. Meng;G. Ceder

  • Towards more accurate First Principles prediction of redox potentials in transition-metal compounds with LDA+U

    Fei Zhou;Matteo Cococcioni;Chris A. Marianetti;Dane Morgan

Frequent Co-Authors

Gerbrand Ceder
Gerbrand Ceder University of California, Berkeley
John H. Booske
John H. Booske University of Wisconsin–Madison
Paul M. Voyles
Paul M. Voyles University of Wisconsin–Madison
Thomas F. Kuech
Thomas F. Kuech University of Wisconsin–Madison
Stefano Curtarolo
Stefano Curtarolo Duke University
Chris Wolverton
Chris Wolverton Northwestern University
Brian D. Wirth
Brian D. Wirth University of Tennessee at Knoxville
Kristin A. Persson
Kristin A. Persson Lawrence Berkeley National Laboratory
Michael D. Biegalski
Michael D. Biegalski Oak Ridge National Laboratory

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Best Scientists Citing Dane Morgan

Trending Scientists

Recently Published Articles