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Subramanian K. R. S. Sankaranarayanan

Subramanian K. R. S. Sankaranarayanan

D-Index & Metrics

Materials Science

D-Index
50
Citations
10117
World Ranking
10169
National Ranking
2437

Subramanian K. R. S. Sankaranarayanan 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 Subramanian K. R. S. Sankaranarayanan 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: 298 publications — 59th percentile

59% of scientists in this discipline score the same or lower.

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

Subramanian K. R. S. Sankaranarayanan 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 Subramanian K. R. S. Sankaranarayanan 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: 50 D-Index — 22nd percentile

22% of scientists in this discipline score the same or lower.

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

Overview

Subramanian K. R. S. Sankaranarayanan is affiliated with the University of Illinois at Chicago in the United States. Their research spans multiple intersecting fields, with a focus on materials science and engineering. Key areas of study include materials chemistry, electrical and electronic engineering, molecular biology, atomic and molecular physics and optics, and biomedical engineering.

The scientist's work covers various advanced topics within materials science and related disciplines. Main research themes include machine learning applications in materials science, advanced memory and neural computing, diamond and carbon-based materials research, graphene research and applications, transition metal oxide nanomaterials, protein structure and dynamics, and computational drug discovery methods.

Frequent collaborators in their research include Sukriti Manna, Henry Chan, Troy D. Loeffler, Suvo Banik, and Mathew J. Cherukara. These collaborations have contributed to a substantial volume of research outputs across different venues.

Subramanian K. R. S. Sankaranarayanan has published extensively in well-known scientific journals and platforms. Frequent publication venues include:

  • arXiv (Cornell University)
  • Nature Communications
  • npj Computational Materials
  • Nano Letters
  • Zenodo (CERN European Organization for Nuclear Research)

Some of their recent notable papers are:

  • "Machine learning enabled autonomous microstructural characterization in 3D samples" (2020), published in npj Computational Materials
  • "High-Entropy 2D Carbide MXenes: TiVNbMoC 3 and TiVCrMoC 3" (2021), published in ACS Nano
  • "Reconfigurable perovskite nickelate electronics for artificial intelligence" (2022), published in Science
  • "Machine learning overcomes human bias in the discovery of self-assembling peptides" (2022), published in Nature Chemistry
  • "Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies" (2020), published in The Journal of Physical Chemistry Letters

Best Publications

  • Macroscale superlubricity enabled by graphene nanoscroll formation

    Diana Berman;Sanket A. Deshmukh;Subramanian K. R. S. Sankaranarayanan;Ali Erdemir

  • Carbon-based tribofilms from lubricating oils

    Ali Erdemir;Giovanni Ramirez;Osman L. Eryilmaz;Badri Narayanan

  • Machine learning enabled autonomous microstructural characterization in 3D samples

    Henry Chan;Mathew Cherukara;Troy D. Loeffler;Badri Narayanan;Badri Narayanan

  • High-Entropy 2D Carbide MXenes: TiVNbMoC 3 and TiVCrMoC 3

    Srinivasa Kartik Nemani;Bowen Zhang;Brian C. Wyatt;Zachary D. Hood

  • Extraordinary Macroscale Wear Resistance of One Atom Thick Graphene Layer

    Diana Berman;Sanket A. Deshmukh;Subramanian K. R. S. Sankaranarayanan;Ali Erdemir

  • Operando tribochemical formation of onion-like-carbon leads to macroscale superlubricity

    Diana Berman;Diana Berman;Badri Narayanan;Mathew J. Cherukara;Subramanian K. R. S. Sankaranarayanan

  • Reconfigurable perovskite nickelate electronics for artificial intelligence

    Unknown

  • Molecular dynamics simulation study of the melting of Pd-Pt nanoclusters

    Subramanian K. R. S. Sankaranarayanan;Venkat R. Bhethanabotla;Babu Joseph

  • Role of solvation dynamics and local ordering of water in inducing conformational transitions in poly(N-isopropylacrylamide) oligomers through the LCST.

    Sanket A. Deshmukh;Subramanian K. R. S. Sankaranarayanan;Kamlesh Suthar;Derrick C. Mancini

  • Perovskite nickelates as electric-field sensors in salt water

    Zhen Zhang;Derek Schwanz;Badri Narayanan;Michele Kotiuga

  • Crude-Oil-Repellent Membranes by Atomic Layer Deposition: Oxide Interface Engineering.

    Hao-Cheng Yang;Yunsong Xie;Henry Chan;Badri Narayanan

  • Machine Learning Force Field Parameters from Ab Initio Data

    Ying Li;Hui Li;Frank C. Pickard;Badri Narayanan

  • Quantitative 3D evolution of colloidal nanoparticle oxidation in solution

    Yugang Sun;Xiaobing Zuo;Subramanian K. R. S. Sankaranarayanan;Sheng Peng

  • Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data

    Henry Chan;Badri Narayanan;Badri Narayanan;Mathew J. Cherukara;Fatih G. Sen

  • Self-Improving Anode for Lithium-Ion Batteries Based on Amorphous to Cubic Phase Transition in TiO2 Nanotubes

    Hui Xiong;Handan Yildirim;Elena V. Shevchenko;Vitali B. Prakapenka

  • Trends in Na-Ion Solvation with Alkyl-Carbonate Electrolytes for Sodium-Ion Batteries: Insights from First-Principles Calculations

    Mehdi Shakourian-Fard;Ganesh Kamath;Kassiopeia Smith;Hui Xiong

  • Flow induced by acoustic streaming on surface-acoustic-wave devices and its application in biofouling removal: a computational study and comparisons to experiment.

    Subramanian K. R. S. Sankaranarayanan;Stefan Cular;Venkat R. Bhethanabotla;Babu Joseph

  • Habituation based synaptic plasticity and organismic learning in a quantum perovskite

    Fan Zuo;Priyadarshini Panda;Michele Kotiuga;Jiarui Li

  • Ab Initio-Based Bond Order Potential to Investigate Low Thermal Conductivity of Stanene Nanostructures.

    Mathew J. Cherukara;Badri Narayanan;Alper Kinaci;Kiran Sasikumar

  • Metal-induced rapid transformation of diamond into single and multilayer graphene on wafer scale

    Diana Berman;Sanket A. Deshmukh;Badri Narayanan;Subramanian K. R. S. Sankaranarayanan

  • High-Entropy 2D Carbide MXenes

    Nemani Sk;Zhang B;Wyatt Bc;Hood Zd

Frequent Co-Authors

Shriram Ramanathan
Shriram Ramanathan Rutgers, The State University of New Jersey
Maria K. Y. Chan
Maria K. Y. Chan Argonne National Laboratory
Fatih G. Sen
Fatih G. Sen Argonne National Laboratory
Stephen K. Gray
Stephen K. Gray Victoria University
Jeffrey Greeley
Jeffrey Greeley Purdue University West Lafayette
Hua Zhou
Hua Zhou Argonne National Laboratory
Anirudha V. Sumant
Anirudha V. Sumant Argonne National Laboratory
Kaushik Roy
Kaushik Roy Purdue University West Lafayette
Jianguo Wen
Jianguo Wen Argonne National Laboratory
Ali Erdemir
Ali Erdemir Texas A&M University

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