World's Best Scientists 2026 revealed!
O. Anatole von Lilienfeld

O. Anatole von Lilienfeld

D-Index & Metrics

Chemistry

D-Index
59
Citations
18774
World Ranking
9995
National Ranking
265

O. Anatole von Lilienfeld 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 O. Anatole von Lilienfeld 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: 182 publications — 26th percentile

26% of scientists in this discipline score the same or lower.

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

O. Anatole von Lilienfeld 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 O. Anatole von Lilienfeld 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: 59 D-Index — 44th percentile

44% of scientists in this discipline score the same or lower.

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

Overview

O. Anatole von Lilienfeld is affiliated with the University of Toronto in Canada and specializes in fields intersecting materials science and chemistry. Their research encompasses a broad spectrum of topics focused primarily on computational methods and machine learning applications within these scientific domains.

Their main fields of study include:

  • Materials Science
  • Chemistry

Subfields of study linked to their work cover:

  • Materials Chemistry
  • Atomic and Molecular Physics, and Optics
  • Computational Theory and Mathematics
  • Physical and Theoretical Chemistry
  • Molecular Biology

The core topics addressed in their research are:

  • Machine Learning in Materials Science
  • Computational Drug Discovery Methods
  • Advanced Chemical Physics Studies
  • Protein Structure and Dynamics
  • Various Chemistry Research Topics
  • Spectroscopy and Quantum Chemical Studies
  • Catalysis and Oxidation Reactions

Among their frequent coauthors are:

  • Guido Falk von Rudorff
  • Stefan Heinen
  • Danish Khan
  • Dominik Lemm
  • Max Schwilk

Von Lilienfeld has published extensively in several scientific venues, with frequent appearances in:

  • arXiv (Cornell University)
  • The Journal of Chemical Physics
  • Zenodo (CERN European Organization for Nuclear Research)
  • Machine Learning Science and Technology
  • Journal of Chemical Theory and Computation

Recent notable papers include:

  • "FCHL revisited: Faster and more accurate quantum machine learning" (2020) in The Journal of Chemical Physics
  • "Quantum machine learning using atom-in-molecule-based fragments selected on the fly" (2020) in Nature Chemistry
  • "Retrospective on a decade of machine learning for chemical discovery" (2020) in Nature Communications
  • "The central role of density functional theory in the AI age" (2023) in Science
  • "Ab Initio Machine Learning in Chemical Compound Space" (2021) in Chemical Reviews

In addition to research articles, the scientist has contributed to book literature, including:

  • "Machine Learning Meets Quantum Physics" (2020), published by Springer Science+Business Media

Best Publications

  • Quantum chemistry structures and properties of 134 kilo molecules

    Raghunathan Ramakrishnan;Pavlo O. Dral;Pavlo O. Dral;Matthias Rupp;O. Anatole von Lilienfeld

  • Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

    Katja Hansen;Franziska Biegler;Raghunathan Ramakrishnan;Wiktor Pronobis

  • Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

    Felix A. Faber;Luke Hutchison;Bing Huang;Justin Gilmer

  • Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

    Katja Hansen;Grégoire Montavon;Franziska Biegler;Siamac Fazli

  • Optimization of Effective Atom Centered Potentials for London Dispersion Forces in Density Functional Theory

    O. Anatole von Lilienfeld;Ivano Tavernelli;Ursula Rothlisberger;Daniel Sebastiani

  • Crystal structure representations for machine learning models of formation energies

    Felix Faber;Alexander Lindmaa;O. Anatole von Lilienfeld;Rickard Armiento

  • Long Range Interactions in Nanoscale Science.

    Roger H. French;V. Adrian Parsegian;Rudolf Podgornik;Rick F. Rajter

  • Machine Learning Energies of 2 Million Elpasolite (ABC_{2}D_{6}) Crystals.

    Felix A. Faber;Alexander Lindmaa;O. Anatole von Lilienfeld;O. Anatole von Lilienfeld;Rickard Armiento

  • FCHL revisited: Faster and more accurate quantum machine learning

    Anders Steen Christensen;Lars Andersen Bratholm;Felix A. Faber;O. Anatole Von Lilienfeld

  • Understanding molecular representations in machine learning: The role of uniqueness and target similarity

    Bing Huang;O. Anatole von Lilienfeld

  • Two- and three-body interatomic dispersion energy contributions to binding in molecules and solids

    O. Anatole von Lilienfeld;Alexandre Tkatchenko

  • Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

    Matthias Rupp;Raghunathan Ramakrishnan;O. Anatole von Lilienfeld

  • Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

    O. Anatole von Lilienfeld;O. Anatole von Lilienfeld;Raghunathan Ramakrishnan;Matthias Rupp;Aaron Knoll

  • Collective many-body van der Waals interactions in molecular systems

    Robert A. DiStasio;O. Anatole von Lilienfeld;Alexandre Tkatchenko

  • Quantum Machine Learning in Chemical Compound Space.

    Unknown

  • Quantum machine learning using atom-in-molecule-based fragments selected on the fly

    Bing Huang;O. Anatole von Lilienfeld

  • Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts

    Benjamin Meyer;Boodsarin Sawatlon;Stefan Niklaus Heinen;Stefan Niklaus Heinen;O. Anatole von Lilienfeld;O. Anatole von Lilienfeld

  • The central role of density functional theory in the AI age

    Unknown

  • Library of dispersion-corrected atom-centered potentials for generalized gradient approximation functionals: Elements H, C, N, O, He, Ne, Ar, and Kr

    I-Chun Lin;Maurício D. Coutinho-Neto;Camille Felsenheimer;O. Anatole von Lilienfeld

  • First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties

    O. Anatole von Lilienfeld

  • Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

    Tristan Bereau;Robert A. DiStasio;Alexandre Tkatchenko;O. Anatole von Lilienfeld

  • Big Data meets Quantum Chemistry Approximations: The $\Delta$-Machine Learning Approach

    Raghunathan Ramakrishnan;Pavlo O. Dral;Matthias Rupp;O. Anatole von Lilienfeld

Frequent Co-Authors

Alexandre Tkatchenko
Alexandre Tkatchenko University of Luxembourg
Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Matthias Rupp
Matthias Rupp Luxembourg Institute of Science and Technology
Angelos Michaelides
Angelos Michaelides University of Cambridge
Ursula Rothlisberger
Ursula Rothlisberger École Polytechnique Fédérale de Lausanne
Denis Andrienko
Denis Andrienko Max Planck Society
Ivano Tavernelli
Ivano Tavernelli IBM (United States)
Andrew J. Millis
Andrew J. Millis Columbia University
Grégoire Montavon
Grégoire Montavon Freie Universität Berlin
Mark E. Tuckerman
Mark E. Tuckerman New York University

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