World's Best Scientists 2026 revealed!

D-Index & Metrics

Chemistry

D-Index
64
Citations
14162
World Ranking
8065
National Ranking
207

Tom K. Woo publication distribution in Chemistry in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Chemistry in 2026. The highlighted bar marks where Tom K. Woo sits on this spectrum.

61–80 publications: 66 scientists 81–100 publications: 302 scientists 101–120 publications: 623 scientists 121–140 publications: 918 scientists 141–160 publications: 1,218 scientists 161–180 publications: 1,350 scientists 181–200 publications: 1,344 scientists 201–220 publications: 1,281 scientists 221–240 publications: 1,216 scientists 241–260 publications: 1,100 scientists 261–280 publications: 979 scientists 281–300 publications: 939 scientists 301–320 publications: 764 scientists 321–340 publications: 643 scientists 341–360 publications: 628 scientists 361–380 publications: 522 scientists 381–400 publications: 459 scientists 401–420 publications: 397 scientists 421–440 publications: 327 scientists 441–460 publications: 270 scientists 461–480 publications: 265 scientists 481–500 publications: 252 scientists 501–520 publications: 201 scientists 521–540 publications: 185 scientists 541–560 publications: 148 scientists 561–580 publications: 148 scientists 581–600 publications: 132 scientists 601–620 publications: 114 scientists 621–640 publications: 104 scientists 641–660 publications: 91 scientists 661–680 publications: 92 scientists 681–700 publications: 73 scientists 701–720 publications: 57 scientists 721–740 publications: 54 scientists 741–760 publications: 67 scientists 761–780 publications: 45 scientists 781–800 publications: 46 scientists 801–820 publications: 39 scientists 821–840 publications: 32 scientists 841–860 publications: 36 scientists 861–880 publications: 29 scientists 881–900 publications: 26 scientists 901–920 publications: 24 scientists 921–940 publications: 14 scientists 941–960 publications: 23 scientists 961–980 publications: 28 scientists 981–1,000 publications: 15 scientists 1,001–1,020 publications: 29 scientists 1,021–1,040 publications: 12 scientists 1,041–1,060 publications: 19 scientists 1,061–1,080 publications: 12 scientists 1,081–1,100 publications: 6 scientists 1,101–1,120 publications: 8 scientists 1,121–1,140 publications: 12 scientists 1,141–1,160 publications: 5 scientists 1,161–1,180 publications: 6 scientists 1,181–1,200 publications: 14 scientists 1,201–1,220 publications: 7 scientists 1,221–1,240 publications: 2 scientists 1,241–1,260 publications: 6 scientists 1,261–1,280 publications: 4 scientists 1,281–1,294 publications: 6 scientists 1,295+ publications: 100 scientists
61 publications 1,295+

This scientist: 180 publications — 25th percentile

25% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,295 publications or more.

Tom K. Woo D-index placement in Chemistry in 2026

The chart shows the D-index (discipline H-index) distribution of Chemistry scientists ranked by Research.com in 2026. The highlighted bar marks where Tom K. Woo sits on this spectrum.

40–41 D-Index: 289 scientists 42–43 D-Index: 612 scientists 44–45 D-Index: 808 scientists 46–47 D-Index: 776 scientists 48–49 D-Index: 835 scientists 50–51 D-Index: 861 scientists 52–53 D-Index: 872 scientists 54–55 D-Index: 933 scientists 56–57 D-Index: 1,051 scientists 58–59 D-Index: 930 scientists 60–61 D-Index: 882 scientists 62–63 D-Index: 834 scientists 64–65 D-Index: 731 scientists 66–67 D-Index: 775 scientists 68–69 D-Index: 683 scientists 70–71 D-Index: 646 scientists 72–73 D-Index: 561 scientists 74–75 D-Index: 501 scientists 76–77 D-Index: 437 scientists 78–79 D-Index: 388 scientists 80–81 D-Index: 354 scientists 82–83 D-Index: 292 scientists 84–85 D-Index: 275 scientists 86–87 D-Index: 254 scientists 88–89 D-Index: 235 scientists 90–91 D-Index: 185 scientists 92–93 D-Index: 192 scientists 94–95 D-Index: 155 scientists 96–97 D-Index: 163 scientists 98–99 D-Index: 125 scientists 100–101 D-Index: 105 scientists 102–103 D-Index: 105 scientists 104–105 D-Index: 112 scientists 106–107 D-Index: 88 scientists 108–109 D-Index: 68 scientists 110–111 D-Index: 69 scientists 112–113 D-Index: 65 scientists 114–115 D-Index: 79 scientists 116–117 D-Index: 61 scientists 118–119 D-Index: 44 scientists 120–121 D-Index: 37 scientists 122–123 D-Index: 40 scientists 124–125 D-Index: 33 scientists 126–127 D-Index: 26 scientists 128–129 D-Index: 34 scientists 130–131 D-Index: 35 scientists 132–133 D-Index: 25 scientists 134–135 D-Index: 27 scientists 136–137 D-Index: 17 scientists 138–139 D-Index: 16 scientists 140–141 D-Index: 20 scientists 142–143 D-Index: 20 scientists 144–145 D-Index: 15 scientists 146–147 D-Index: 9 scientists 148–149 D-Index: 9 scientists 150–151 D-Index: 16 scientists 152–153 D-Index: 11 scientists 154–155 D-Index: 9 scientists 156–157 D-Index: 3 scientists 158 D-Index: 3 scientists 159+ D-Index: 98 scientists
40 D-Index 159+

This scientist: 64 D-Index — 56th percentile

56% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 159 D-Index or more.

Overview

Tom K. Woo is affiliated with the University of Ottawa in Canada, with a research focus encompassing multiple aspects of materials science, chemistry, and engineering. Their body of work spans 18 publications in materials science, 14 in chemistry, and 13 in engineering, illustrating a multidisciplinary approach to research.

Their expertise extends notably into subfields such as materials chemistry and inorganic chemistry, with 18 and 14 publications respectively. They have also contributed to mechanical engineering, environmental chemistry, and biomedical engineering.

The primary research topics that Tom K. Woo engages with include:

  • Metal-Organic Frameworks: Synthesis and Applications
  • Machine Learning in Materials Science
  • Carbon Dioxide Capture Technologies
  • X-ray Diffraction in Crystallography
  • Methane Hydrates and Related Phenomena
  • Covalent Organic Framework Applications
  • Phase Equilibria and Thermodynamics

Their recent publications reflect this diverse interest and include:

  • A scalable metal-organic framework as a durable physisorbent for carbon dioxide capture, 2021, Science
  • Inverse design of nanoporous crystalline reticular materials with deep generative models, 2021, Nature Machine Intelligence
  • Prediction of MOF Performance in Vacuum Swing Adsorption Systems for Postcombustion CO2 Capture Based on Integrated Molecular Simulations, Process Optimizations, and Machine Learning Models, 2020, Environmental Science & Technology
  • ARC-MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning, 2023, Chemistry of Materials
  • High-Performing Deep Learning Regression Models for Predicting Low-Pressure CO2 Adsorption Properties of Metal-Organic Frameworks, 2020, The Journal of Physical Chemistry C

Tom K. Woo collaborates frequently with several co-authors, including:

  • Marco Gibaldi
  • Jake Burner
  • Andrew J. P. White
  • Ohmin Kwon
  • Peter G. Boyd

Their research contributions have been published in various scientific venues. Notable frequent publication venues include:

  • Zenodo (CERN European Organization for Nuclear Research)
  • The Journal of Chemical Physics
  • ACS Applied Materials & Interfaces
  • Science
  • Nature Machine Intelligence

Best Publications

  • Direct Observation and Quantification of CO2 Binding Within an Amine-Functionalized Nanoporous Solid

    Ramanathan Vaidhyanathan;Simon S. Iremonger;George K. H. Shimizu;Peter G. Boyd

  • Catalytic intermolecular direct arylation of perfluorobenzenes.

    Marc Lafrance;Christopher N Rowley;Tom K Woo;Keith Fagnou

  • Data-driven design of metal-organic frameworks for wet flue gas CO2 capture.

    Peter G. Boyd;Arunraj Chidambaram;Enrique García-Díez;Christopher P. Ireland

  • The Role of Bulky Substituents in Brookhart-Type Ni(II) Diimine Catalyzed Olefin Polymerization: A Combined Density Functional Theory and Molecular Mechanics Study

    Liqun Deng;Tom K. Woo;Luigi Cavallo;and Peter M. Margl

  • Electrostatic Potential Derived Atomic Charges for Periodic Systems Using a Modified Error Functional

    Carlos Campañá;Bastien Mussard;Tom K. Woo

  • Inverse design of nanoporous crystalline reticular materials with deep generative models

    Zhenpeng Yao;Zhenpeng Yao;Benjamín Sánchez-Lengeling;N. Scott Bobbitt;Benjamin J. Bucior

  • Atomic and electronic structure of unreduced and reduced CeO2 surfaces: A first-principles study

    Zongxian Yang;Tom K. Woo;Micael Baudin;Kersti Hermansson

  • A density functional study of chain growing and chain terminating steps in olefin polymerization by metallocene and constrained geometry catalysts

    T. K. Woo;L. Fan;T. Ziegler

  • Molecular Mechanisms for the Functionality of Lubricant Additives

    Nicholas J. Mosey;Martin H. Müser;Tom K. Woo

  • Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture.

    Michael Fernandez;Peter G. Boyd;Thomas D. Daff;Mohammad Zein Aghaji

  • Large-Scale Quantitative Structure–Property Relationship (QSPR) Analysis of Methane Storage in Metal–Organic Frameworks

    Michael Fernandez;Tom K. Woo;Christopher E. Wilmer;Randall Q. Snurr

  • A Combined Car−Parrinello QM/MM Implementation for ab Initio Molecular Dynamics Simulations of Extended Systems: Application to Transition Metal Catalysis

    Tom K. Woo;Peter M. Margl;Peter E. Blöchl;Tom Ziegler

  • A Density Functional Study on the Origin of the Propagation Barrier in the Homogeneous Ethylene Polymerization with Kaminsky-Type Catalysts

    John C. W. Lohrenz;Tom K. Woo;Tom Ziegler

  • Implementation of the IMOMM methodology for performing combined QM/MM molecular dynamics simulations and frequency calculations

    Tom K. Woo;Luigi Cavallo;Tom Ziegler

  • Prediction of MOF Performance in Vacuum Swing Adsorption Systems for Postcombustion CO2 Capture Based on Integrated Molecular Simulations, Process Optimizations, and Machine Learning Models.

    Thomas D. Burns;Kasturi Nagesh Pai;Sai Gokul Subraveti;Sean P. Collins

  • Effects of Zr doping on stoichiometric and reduced ceria: A first-principles study

    Zongxian Yang;Tom K. Woo;Kersti Hermansson

  • Combined Static and Dynamic Density Functional Study of the Ti(IV) Constrained Geometry Catalyst (CpSiH2NH)TiR+. 1. Resting States and Chain Propagation

    Tom K. Woo;Peter M. Margl;John C. W. Lohrenz;and Peter E. Blöchl

  • A single-ligand ultra-microporous MOF for precombustion CO2 capture and hydrogen purification

    Shyamapada Nandi;Phil De Luna;Thomas D. Daff;Jens Rother

  • Robust Machine Learning Models for Predicting High CO2 Working Capacity and CO2/H2 Selectivity of Gas Adsorption in Metal Organic Frameworks for Precombustion Carbon Capture

    Hana Dureckova;Mykhaylo Krykunov;Mohammad Zein Aghaji;Tom K. Woo

  • Static and ab Initio Molecular Dynamics Study of the Titanium(IV)-Constrained Geometry Catalyst (CpSiH2NH)Ti-R+. 2. Chain Termination and Long Chain Branching

    Tom K. Woo;Peter M. Margl;Tom Ziegler;Peter E. Blöchl

  • Hydrogen-bonding alcohol-water interactions in binary ethanol, 1-propanol, and 2-propanol+methane structure II clathrate hydrates.

    Saman Alavi;Satoshi Takeya;Ryo Ohmura;Tom K. Woo

  • A generalized method for constructing hypothetical nanoporous materials of any net topology from graph theory

    Peter G. Boyd;Tom K. Woo

Frequent Co-Authors

Saman Alavi
Saman Alavi University of Ottawa
Tom Ziegler
Tom Ziegler University of Calgary
John A. Ripmeester
John A. Ripmeester National Research Council Canada
Luigi Cavallo
Luigi Cavallo King Abdullah University of Science and Technology
Kersti Hermansson
Kersti Hermansson Uppsala University
George K. H. Shimizu
George K. H. Shimizu University of Calgary
Tom Burns
Tom Burns University of Oxford
Yining Huang
Yining Huang University of Western Ontario
Darrin S. Richeson
Darrin S. Richeson University of Ottawa
Ilia Korobkov
Ilia Korobkov Saudi Arabia Basic Industries (Saudi Arabia)

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