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Heather J. Kulik

Heather J. Kulik

MIT
United States

D-Index & Metrics

Chemistry

D-Index
54
Citations
10968
World Ranking
12567
National Ranking
3343

Heather J. Kulik 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 Heather J. Kulik 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: 390 publications — 79th percentile

79% of scientists in this discipline score the same or lower.

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

Heather J. Kulik 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 Heather J. Kulik 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: 54 D-Index — 31st percentile

31% of scientists in this discipline score the same or lower.

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

Overview

Heather J. Kulik is affiliated with MIT in the United States and has a research focus primarily in materials science and chemistry. Their work encompasses significant contributions to materials chemistry, inorganic chemistry, molecular biology, computational theory and mathematics, and organic chemistry.

The main topics of the scientist's work include:

  • Machine Learning in Materials Science
  • Computational Drug Discovery Methods
  • Metal-Organic Frameworks: Synthesis and Applications
  • Catalysis and Oxidation Reactions
  • X-ray Diffraction in Crystallography
  • Protein Structure and Dynamics
  • Catalytic Processes in Materials Science

Recent published papers by Heather J. Kulik include:

  • Understanding the diversity of the metal-organic framework ecosystem, 2020, Nature Communications
  • Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning, 2021, Chemical Reviews
  • Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization, 2020, ACS Central Science
  • Roadmap on Machine learning in electronic structure, 2022, Electronic Structure
  • Fluids and Electrolytes under Confinement in Single-Digit Nanopores, 2023, Chemical Reviews

Frequent coauthors with whom the scientist has collaborated extensively include:

  • Aditya Nandy
  • Chenru Duan
  • Ilia Kevlishvili
  • Gianmarco Terrones
  • David W. Kastner

Publication venues where the scientist has released multiple works include:

  • Zenodo (CERN European Organization for Nuclear Research)
  • arXiv (Cornell University)
  • Journal of the American Chemical Society
  • The Cambridge Structural Database
  • ACS Catalysis

Heather J. Kulik has also contributed to the literature through book publications, including a title published by the American Chemical Society:

  • Machine Learning in Chemistry, 2020

Best Publications

  • Density functional theory in transition-metal chemistry: A self-consistent Hubbard U approach

    Heather J. Kulik;Matteo Cococcioni;Damian A. Scherlis;Nicola N. Marzari

  • Understanding the diversity of the metal-organic framework ecosystem

    Seyed Mohamad Moosavi;Seyed Mohamad Moosavi;Aditya Nandy;Kevin Maik Jablonka;Daniele Ongari

  • Protection of tissue physicochemical properties using polyfunctional crosslinkers.

    Young Gyun Park;Young Gyun Park;Chang Ho Sohn;Chang Ho Sohn;Ritchie Chen;Ritchie Chen;Margaret McCue;Margaret McCue

  • Critical Knowledge Gaps in Mass Transport through Single-Digit Nanopores: A Review and Perspective

    Samuel Faucher;Narayana R Aluru;Martin Z. Bazant;Daniel Blankschtein

  • Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships

    Jon Paul Janet;Heather J. Kulik

  • Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning.

    Aditya Nandy;Chenru Duan;Michael G Taylor;Fang Liu

  • molSimplify: A toolkit for automating discovery in inorganic chemistry

    Efthymios I. Ioannidis;Terry Z. H. Gani;Heather J. Kulik

  • Mechanically triggered heterolytic unzipping of a low-ceiling-temperature polymer.

    Charles E. Diesendruck;Gregory I. Peterson;Heather J. Kulik;Joshua A. Kaitz

  • Perspective: Treating electron over-delocalization with the DFT+U method

    Heather J. Kulik

  • How Large Should the QM Region Be in QM/MM Calculations? The Case of Catechol O-Methyltransferase

    Heather J. Kulik;Jianyu Zhang;Judith P. Klinman;Todd J. Martínez;Todd J. Martínez

  • A quantitative uncertainty metric controls error in neural network-driven chemical discovery

    Jon Paul Janet;Chenru Duan;Tzuhsiung Yang;Aditya Nandy

  • Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

    Aditya Nandy;Chenru Duan;Jon Paul Janet;Stefan Gugler;Stefan Gugler

  • Understanding and Breaking Scaling Relations in Single-Site Catalysis: Methane to Methanol Conversion by FeIV═O

    Terry Z. H. Gani;Heather J. Kulik

  • Machine learning reveals key ion selectivity mechanisms in polymeric membranes with subnanometer pores

    Unknown

  • Anion-Selective Redox Electrodes: Electrochemically Mediated Separation with Heterogeneous Organometallic Interfaces

    Xiao Su;Heather J. Kulik;Timothy F. Jamison;T. Alan Hatton

  • Ab Initio Quantum Chemistry for Protein Structures

    Heather J. Kulik;Heather J. Kulik;Nathan Luehr;Nathan Luehr;Ivan S. Ufimtsev;Ivan S. Ufimtsev;Todd J. Martinez;Todd J. Martinez

  • Ionization behavior of nanoporous polyamide membranes.

    Cody L. Ritt;Jay R. Werber;Mengyi Wang;Zhongyue Yang

  • Quantum Chemistry for Solvated Molecules on Graphical Processing Units Using Polarizable Continuum Models

    Fang Liu;Fang Liu;Nathan Luehr;Nathan Luehr;Heather J. Kulik;Heather J. Kulik;Todd J. Martínez;Todd J. Martínez

  • Systematic study of first-row transition-metal diatomic molecules: a self-consistent DFT+U approach.

    Heather J. Kulik;Nicola Marzari

  • Towards quantifying the role of exact exchange in predictions of transition metal complex properties.

    Efthymios I. Ioannidis;Heather J. Kulik

  • Systematic Quantum Mechanical Region Determination in QM/MM Simulation

    Maria Karelina;Heather Janine Kulik

  • A self-consistent Hubbard U density-functional theory approach to the addition-elimination reactions of hydrocarbons on bare FeO+.

    Heather J. Kulik;Nicola N. Marzari

  • Spatially extended Kondo state in magnetic molecules induced by interfacial charge transfer.

    U. G. E. Perera;H. J. Kulik;V. Iancu;L. G. G. V. Dias da Silva;L. G. G. V. Dias da Silva

  • Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry.

    Jon Paul Janet;Fang Liu;Aditya Nandy;Chenru Duan

  • Accurate potential energy surfaces with a DFT+U(R) approach.

    Heather J. Kulik;Nicola Marzari

Frequent Co-Authors

Nicola Marzari
Nicola Marzari École Polytechnique Fédérale de Lausanne
Todd J. Martínez
Todd J. Martínez Stanford University
Catherine L. Drennan
Catherine L. Drennan MIT
Giulia Galli
Giulia Galli University of Chicago
Xin Jin
Xin Jin South China University of Technology
Byung Kook Lim
Byung Kook Lim University of California, San Diego
Michael S. Strano
Michael S. Strano MIT
Berend Smit
Berend Smit École Polytechnique Fédérale de Lausanne
David Prendergast
David Prendergast Lawrence Berkeley National Laboratory
T. Alan Hatton
T. Alan Hatton MIT

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Best Scientists Citing Heather J. Kulik

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Publications: 15

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Publications: 14

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