World's Best Scientists 2026 revealed!

D-Index & Metrics

Materials Science

D-Index
58
Citations
10649
World Ranking
7775
National Ranking
1927

Ming Hu 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 Ming Hu 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: 217 publications — 36th percentile

36% of scientists in this discipline score the same or lower.

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

Ming Hu 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 Ming Hu 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: 58 D-Index — 41st percentile

41% of scientists in this discipline score the same or lower.

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

Overview

Ming Hu is affiliated with the University of South Carolina in the United States and primarily works in the field of Materials Science. Their scientific contributions encompass multiple subfields, including Materials Chemistry, Electrical and Electronic Engineering, Artificial Intelligence, Mechanical Engineering, and Molecular Biology.

Their research portfolio covers a range of topics, with a particular emphasis on thermal properties of materials, advanced thermoelectric materials and devices, and the application of machine learning in materials science. Other focal topics include X-ray diffraction in crystallography, graphene research and applications, privacy-preserving technologies in data, and 2D materials and applications.

Recent publications by Ming Hu include the following papers:

  • "Thermal transport properties of GaN with biaxial strain and electron-phonon coupling," published in 2020 in the Journal of Applied Physics
  • "Thermodynamic and Transport Properties of LiF and FLiBe Molten Salts with Deep Learning Potentials," published in 2021 in ACS Applied Materials & Interfaces
  • "Physics guided deep learning for generative design of crystal materials with symmetry constraints," published in 2023 in npj Computational Materials
  • "High-Throughput Computation of New Carbon Allotropes with Diverse Hybridization and Ultrahigh Hardness," published in 2021 in Crystals
  • "High-throughput computational evaluation of lattice thermal conductivity using an optimized Slack model," published in 2022 in Materials Advances

Ming Hu frequently publishes in venues such as arXiv (Cornell University), UNC Libraries, npj Computational Materials, Journal of Materials Chemistry C, and The Cambridge Structural Database.

The scientist often collaborates with researchers including Mohammed Al-Fahdi, Jianjun Hu, Guangzhao Qin, Kunpeng Yuan, and Alejandro Rodriguez, reflecting ongoing partnerships across various projects and publications.

Best Publications

  • Anisotropic intrinsic lattice thermal conductivity of phosphorene from first principles

    Guangzhao Qin;Qing-Bo Yan;Zhenzhen Qin;Sheng-Ying Yue

  • Evaluating explorative prediction power of machine learning algorithms for materials discovery using k -fold forward cross-validation

    Zheng Xiong;Yuxin Cui;Zhonghao Liu;Yong Zhao

  • Thermal conductivity of silicene calculated using an optimized Stillinger-Weber potential

    Xiaoliang Zhang;Han Xie;Ming Hu;Hua Bao

  • Diverse anisotropy of phonon transport in two-dimensional group IV-VI compounds: A comparative study.

    Guangzhao Qin;Guangzhao Qin;Zhenzhen Qin;Wu-Zhang Fang;Li-Chuan Zhang

  • Significant Reduction of Thermal Conductivity in Si/Ge Core-Shell Nanowires

    Ming Hu;Konstantinos P. Giapis;Javier V. Goicochea;Xiaoliang Zhang

  • Hot-pressing induced alignment of boron nitride in polyurethane for composite films with thermal conductivity over 50 Wm −1 K −1

    Cuiping Yu;Cuiping Yu;Wenbin Gong;Wei Tian;Qichong Zhang

  • Si/Ge superlattice nanowires with ultralow thermal conductivity.

    Ming Hu;Dimos Poulikakos

  • Kapitza conductance of silicon–amorphous polyethylene interfaces by molecular dynamics simulations

    Ming Hu;Pawel Keblinski;Patrick K. Schelling

  • Disparate Strain Dependent Thermal Conductivity of Two-dimensional Penta-Structures.

    Huake Liu;Huake Liu;Guangzhao Qin;Yuan Lin;Ming Hu

  • Anomalous thermal response of silicene to uniaxial stretching

    Ming Hu;Xiaoliang Zhang;Dimos Poulikakos

  • Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials

    Yabo Dan;Yong Zhao;Xiang Li;Shaobo Li

  • Large tunability of lattice thermal conductivity of monolayer silicene via mechanical strain

    Han Xie;Tao Ouyang;Éric Germaneau;Guangzhao Qin

  • Diverse anisotropy of phonon transport in two-dimensional IV-VI compounds: A comparative study

    Guangzhao Qin;Zhenzhen Qin;Wu-Zhang Fang;Li-Chuan Zhang

  • Thermal conductivity of silicene from first-principles

    Han Xie;Ming Hu;Hua Bao

  • A facile method to prepare flexible boron nitride/poly(vinyl alcohol) composites with enhanced thermal conductivity

    Jun Zhang;Jun Zhang;Xiaona Wang;Cuiping Yu;Qiulong Li

  • On the Mechanism of Hydrophilicity of Graphene.

    Guo Hong;Yang Han;Thomas M. Schutzius;Yuming Wang

  • Orbitally driven low thermal conductivity of monolayer gallium nitride (GaN) with planar honeycomb structure: a comparative study

    Zhenzhen Qin;Guangzhao Qin;Xu Zuo;Zhihua Xiong

  • Resonant bonding driven giant phonon anharmonicity and low thermal conductivity of phosphorene

    Guangzhao Qin;Xiaoliang Zhang;Sheng-Ying Yue;Zhenzhen Qin

  • Interfacial thermal conductance between silicon and a vertical carbon nanotube

    Ming Hu;Pawel Keblinski;Jian-Sheng Wang;Nachiket Raravikar

  • Full quantification of frequency-dependent interfacial thermal conductance contributed by two- and three-phonon scattering processes from nonequilibrium molecular dynamics simulations

    Yanguang Zhou;Ming Hu

  • Thermal conductivity reduction in core-shell nanowires

    Ming Hu;Xiaoliang Zhang;Xiaoliang Zhang;Konstantinos P. Giapis;Dimos Poulikakos

Frequent Co-Authors

Huimin Wang
Huimin Wang Chinese Academy of Sciences
Yagang Yao
Yagang Yao Nanjing University
Pawel Keblinski
Pawel Keblinski Rensselaer Polytechnic Institute
Jun Zhang
Jun Zhang Inner Mongolia University
G. Jeffrey Snyder
G. Jeffrey Snyder Northwestern University
Jianxin Zhong
Jianxin Zhong Xiangtan University
Dawei Tang
Dawei Tang Dalian University of Technology
Qiang Wang
Qiang Wang Ministry of Education of the People's Republic of China
Bruno Michel
Bruno Michel IBM Research - Zurich

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