World's Best Scientists 2026 revealed!

D-Index & Metrics

Materials Science

D-Index
73
Citations
20789
World Ranking
3815
National Ranking
1042

Rampi Ramprasad 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 Rampi Ramprasad 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: 325 publications — 65th percentile

65% of scientists in this discipline score the same or lower.

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

Rampi Ramprasad 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 Rampi Ramprasad 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: 73 D-Index — 71st percentile

71% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2020 - Fellow of the Materials Research Society For pioneering contributions to the computation, data- and informatics-guided design of materials, and the creation of machine-learning-based tools for on-demand instantaneous predictions of complex materials properties.

Overview

Rampi Ramprasad is affiliated with the Georgia Institute of Technology in the United States. Their research primarily focuses on materials science and engineering, with significant contributions in areas such as materials chemistry, biomedical engineering, electrical and electronic engineering, computational theory and mathematics, and polymers and plastics.

The main topics of Ramprasad's work include:

  • Machine Learning in Materials Science
  • Computational Drug Discovery Methods
  • Fuel Cells and Related Materials
  • Dielectric materials and actuators
  • Advanced Sensor and Energy Harvesting Materials
  • Biodegradable polymer synthesis and properties
  • Synthesis and properties of polymers

Some of Ramprasad's recent papers include:

  • Emerging materials intelligence ecosystems propelled by machine learning, 2020, Nature Reviews Materials
  • Flexible Temperature-Invariant Polymer Dielectrics with Large Bandgap, 2020, Advanced Materials
  • Machine-learning predictions of polymer properties with Polymer Genome, 2020, Journal of Applied Physics
  • Polymer design using genetic algorithm and machine learning, 2020, Computational Materials Science
  • Prediction of water stability of metal-organic frameworks using machine learning, 2020, Nature Machine Intelligence

Frequent coauthors collaborating with Ramprasad include:

  • Tran Doan Huan
  • Lihua Chen
  • Yang Cao
  • Rishi Gurnani
  • Gregory A. Sotzing

Ramprasad often publishes in venues such as:

  • arXiv (Cornell University)
  • npj Computational Materials
  • ACS Applied Materials & Interfaces
  • Advanced Materials
  • Chemistry of Materials

In 2020, Ramprasad was recognized as a Fellow of the Materials Research Society for contributions in computation and data- and informatics-guided design of materials, along with the development of machine-learning-based tools for predicting complex materials properties on demand.

Best Publications

  • Machine learning in materials informatics: recent applications and prospects

    Rampi Ramprasad;Rohit Batra;Ghanshyam Pilania;Ghanshyam Pilania;Arun Mannodi-Kanakkithodi

  • Machine Learning Force Fields: Construction, Validation, and Outlook

    V. Botu;R. Batra;J. Chapman;R. Ramprasad

  • Pathways towards ferroelectricity in hafnia

    Tran Doan Huan;Vinit Sharma;George A. Rossetti;Rampi Ramprasad

  • Machine learning bandgaps of double perovskites

    Ghanshyam Pilania;Arun Mannodi-Kanakkithodi;Blas Uberuaga;Rampi Ramprasad

  • Mesoporous MoO 3- x Material as an Efficient Electrocatalyst for Hydrogen Evolution Reactions

    Zhu Luo;Ran Miao;Tran Doan Huan;Islam M. Mosa;Islam M. Mosa

  • Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions

    Chiho Kim;Anand Chandrasekaran;Tran Doan Huan;Deya Das

  • Adaptive machine learning framework to accelerate ab initio molecular dynamics

    Venkatesh Botu;Rampi Ramprasad

  • Advanced polymeric dielectrics for high energy density applications

    Tran Doan Huan;Steve Boggs;Gilbert Teyssedre;Christian Laurent

  • Machine Learning Strategy for Accelerated Design of Polymer Dielectrics

    Arun Mannodi-Kanakkithodi;Ghanshyam Pilania;Tran Doan Huan;Turab Lookman

  • Machine Learning in Materials Science

    Tim Mueller;Aaron Gilad Kusne;Rampi Ramprasad

  • Rational design of all organic polymer dielectrics

    Vinit Sharma;Chenchen Wang;Robert G. Lorenzini;Rui Ma

  • Emerging materials intelligence ecosystems propelled by machine learning

    Rohit Batra;Le Song;Rampi Ramprasad

  • Solving the electronic structure problem with machine learning

    Anand Chandrasekaran;Deepak Kamal;Rohit Batra;Chiho Kim

  • Physically informed artificial neural networks for atomistic modeling of materials.

    G. P. Purja Pun;R. Batra;R. Ramprasad;Y. Mishin

  • Critical Assessment of the Hildebrand and Hansen Solubility Parameters for Polymers.

    Shruti Venkatram;Chiho Kim;Anand Chandrasekaran;Rampi Ramprasad

  • A universal strategy for the creation of machine learning-based atomistic force fields

    Tran Doan Huan;Rohit Batra;James Chapman;Sridevi Krishnan

  • Machine-learning predictions of polymer properties with Polymer Genome

    Huan Doan Tran;Chiho Kim;Lihua Chen;Anand Chandrasekaran

  • Flexible Temperature-Invariant Polymer Dielectrics with Large Bandgap.

    Chao Wu;Ajinkya A. Deshmukh;Zongze Li;Lihua Chen

  • Learning scheme to predict atomic forces and accelerate materials simulations

    V. Botu;R. Ramprasad

  • Polymer informatics: Current status and critical next steps

    Lihua Chen;Ghanshyam Pilania;Rohit Batra;Tran Doan Huan

  • Magnetic properties of metallic ferromagnetic nanoparticle composites

    R. Ramprasad;P. Zurcher;M. Petras;M. Miller

  • Factors Favoring Ferroelectricity in Hafnia: A First-Principles Computational Study

    Rohit Batra;Tran Doan Huan;Jacob L. Jones;George Rossetti

  • Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond

    Arun Mannodi-Kanakkithodi;Arun Mannodi-Kanakkithodi;Anand Chandrasekaran;Chiho Kim;Tran Doan Huan

  • Machine Learning and Materials Informatics: Recent Applications and Prospects

    Rampi Ramprasad;Rohit Batra;Ghanshyam Pilania;Arun Mannodi-Kanakkithodi

Frequent Co-Authors

Gregory A. Sotzing
Gregory A. Sotzing University of Connecticut
Prabhakar Singh
Prabhakar Singh University of Connecticut
James B. Adams
James B. Adams Arizona State University
Steven A. Boggs
Steven A. Boggs Nonlinear Sysems, Inc.
William F. Schneider
William F. Schneider University of Notre Dame
Artem R. Oganov
Artem R. Oganov Skolkovo Institute of Science and Technology
Priya Vashishta
Priya Vashishta University of Southern California
Rajiv K. Kalia
Rajiv K. Kalia University of Southern California
Sanat K. Kumar
Sanat K. Kumar Columbia University
Blas P. Uberuaga
Blas P. Uberuaga Los Alamos National Laboratory

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