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D-Index & Metrics

Materials Science

D-Index
47
Citations
9270
World Ranking
11065
National Ranking
2601

Elizabeth A. Holm 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 Elizabeth A. Holm 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: 208 publications — 33rd percentile

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

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

Elizabeth A. Holm 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 Elizabeth A. Holm 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: 47 D-Index — 15th percentile

15% of scientists in this discipline score the same or lower.

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

Overview

Elizabeth A. Holm is affiliated with the University of Michigan-Ann Arbor in the United States. Their research spans multiple fields within materials science and engineering, with a focus on the integration of computational methods and material characterization.

The main fields of study for Elizabeth A. Holm include:

  • Materials Science
  • Engineering

Their subfields of study show a strong interdisciplinary approach, involving:

  • Materials Chemistry
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Mechanics of Materials
  • Industrial and Manufacturing Engineering

Central topics in their work cover:

  • Machine Learning in Materials Science
  • Microstructure and mechanical properties
  • Mineral Processing and Grinding
  • Industrial Vision Systems and Defect Detection
  • Microstructure and Mechanical Properties of Steels
  • Electron and X-Ray Spectroscopy Techniques
  • Domain Adaptation and Few-Shot Learning

Their publication record includes numerous papers in several prominent scientific venues where they most frequently publish:

  • Acta Materialia
  • SSRN Electronic Journal
  • JOM
  • arXiv (Cornell University)
  • Annual Review of Materials Research

Recent papers authored or co-authored by Elizabeth A. Holm cover a range of computational and experimental materials science topics. Selected papers include:

  • Recent advances and applications of deep learning methods in materials science, 2022, npj Computational Materials
  • A deep learning approach for complex microstructure inference, 2021, Nature Communications
  • Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image Data, 2021, Integrating materials and manufacturing innovation
  • Machine-Learning Microstructure for Inverse Material Design, 2021, Advanced Science
  • A transfer learning approach for improved classification of carbon nanomaterials from TEM images, 2020, Nanoscale Advances

Collaborations form an important aspect of their research, with frequent co-authors including:

  • Ian Chesser
  • Ryan Cohn
  • Bo Lei
  • Anthony D. Rollett
  • Martin Müller

Best Publications

  • Recent Advances and Applications of Deep Learning Methods in Materials Science

    Kamal Choudhary;Brian DeCost;Chi Chen;Anubhav Jain

  • Survey of computed grain boundary properties in face-centered cubic metals: I. Grain boundary energy

    David L. Olmsted;Stephen M. Foiles;Elizabeth A. Holm

  • Survey of computed grain boundary properties in face-centered cubic metals—II: Grain boundary mobility

    Unknown

  • Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

    Dennis M. Dimiduk;Elizabeth A. Holm;Stephen R. Niezgoda

  • A computer vision approach for automated analysis and classification of microstructural image data

    Brian L. DeCost;Elizabeth A. Holm

  • Computing the mobility of grain boundaries.

    Koenraad G. F. Janssens;Koenraad G. F. Janssens;David Olmsted;Elizabeth A. Holm;Stephen M. Foiles

  • How Grain Growth Stops: A Mechanism for Grain-Growth Stagnation in Pure Materials

    Elizabeth A. Holm;Stephen M. Foiles

  • Grain boundary energies in body-centered cubic metals

    Sutatch Ratanaphan;Sutatch Ratanaphan;David L. Olmsted;Vasily V. Bulatov;Elizabeth A. Holm

  • On misorientation distribution evolution during anisotropic grain growth

    Elizabeth A. Holm;Gregory N. Hassold;Mark A. Miodownik

  • On abnormal subgrain growth and the origin of recrystallization nuclei

    E A Holm;Mark Miodownik;A D Rollett

  • Effects of lattice anisotropy and temperature on domain growth in the two-dimensional Potts model.

    Elizabeth A. Holm;James A. Glazier;David J. Srolovitz;Gary S. Grest

  • Exploring the microstructure manifold: Image texture representations applied to ultrahigh carbon steel microstructures

    Brian L. DeCost;Toby Francis;Elizabeth A. Holm

  • Boundary Mobility and Energy Anisotropy Effects on Microstructural Evolution During Grain Growth

    Moneesh Upmanyu;Gregory N. Hassold;Andrei Kazaryan;Elizabeth A. Holm

  • Comparing grain boundary energies in face-centered cubic metals: Al, Au, Cu and Ni

    Elizabeth A. Holm;David L. Olmsted;Stephen M. Foiles

  • High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel.

    Brian L DeCost;Bo Lei;Toby Francis;Elizabeth A Holm

  • Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis

    Elizabeth A. Holm;Ryan Cohn;Nan Gao;Andrew R. Kitahara

  • Phenomenology of shear-coupled grain boundary motion in symmetric tilt and general grain boundaries

    Eric R. Homer;Eric R. Homer;Stephen M. Foiles;Elizabeth A. Holm;Elizabeth A. Holm;David L. Olmsted

  • Highly parallel computer simulations of particle pinning: zener vindicated

    Mark A. Miodownik;Elizabeth A. Holm;Gregory N. Hassold

  • Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks

    Brian L. DeCost;Harshvardhan Jain;Anthony D. Rollett;Elizabeth A. Holm

  • Comparing calculated and measured grain boundary energies in nickel

    Gregory S. Rohrer;Elizabeth A. Holm;Anthony D. Rollett;Stephen M. Foiles

  • Applied machine learning to predict stress hotspots I: Face centered cubic materials

    Ankita Mangal;Elizabeth A. Holm

  • On boundary misorientation distribution functions and how to incorporate them into three-dimensional models of microstructural evolution

    M Miodownik;A.W Godfrey;E.A Holm;D.A Hughes

  • Comparison of phase-field and Potts models for coarsening processes

    V. Tikare;E. A. Holm;D. Fan;Long-qing Chen

Frequent Co-Authors

Stephen M. Foiles
Stephen M. Foiles Sandia National Laboratories
Anthony D. Rollett
Anthony D. Rollett Carnegie Mellon University
David J. Srolovitz
David J. Srolovitz University of Hong Kong
Peter J. Bentley
Peter J. Bentley University College London
Christopher R. Weinberger
Christopher R. Weinberger Colorado State University
Brad L. Boyce
Brad L. Boyce Sandia National Laboratories
Gregory S. Rohrer
Gregory S. Rohrer Carnegie Mellon University
Peter Gumbsch
Peter Gumbsch Karlsruhe Institute of Technology
Steven J. Plimpton
Steven J. Plimpton Sandia National Laboratories
Paul A. Salvador
Paul A. Salvador Carnegie Mellon University

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