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O. Anatole von Lilienfeld

O. Anatole von Lilienfeld

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Chemistry 59 9995 9041 265 232 182 18774

O. Anatole von Lilienfeld publications per year

The chart shows the history of publications by O. Anatole von Lilienfeld between 2004 and 2025, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. O. Anatole von Lilienfeld published across 22 years, from 2004 to 2025, averaging 12.6 papers a year. Output peaked at 40 publications in 2020. 31 of the 278 publications appeared in the last two years.

No. of publications
10 20 30 40
Bar chart. Horizontal axis: year, 2004 to 2025. Vertical axis: number of publications, 0 to 40. Peak 40 publications in 2020. 2004: 1 publication 2005: 4 publications 2006: 3 publications 2007: 7 publications 2008: 3 publications 2009: 3 publications 2010: 6 publications 2011: 3 publications 2012: 9 publications 2013: 7 publications 2014: 13 publications 2015: 19 publications 2016: 15 publications 2017: 16 publications 2018: 19 publications 2019: 10 publications 2020: 40 publications 2021: 21 publications 2022: 17 publications 2023: 31 publications 2024: 17 publications 2025: 14 publications
2004 2025

278 publications in total across all disciplines

View publications per year as a table
O. Anatole von Lilienfeld: publications per year, 2004 to 2025
Year Publications
2004 1
2005 4
2006 3
2007 7
2008 3
2009 3
2010 6
2011 3
2012 9
2013 7
2014 13
2015 19
2016 15
2017 16
2018 19
2019 10
2020 40
2021 21
2022 17
2023 31
2024 17
2025 14
Total 278
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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.

No. of scientists
250 500 750 1,000 1,250
Bar chart with 63 bars. Horizontal axis: publications, 61–80 to 1,295+. Vertical axis: number of scientists, 0 to 1,350. Most scientists, 1,350, have 161–180 publications. The last bar groups every scientist with 1,295 publications or more. The highlighted bar, 181–200 publications, is where this scientist sits. 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
61–80 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.

View publications distribution as a table
Number of Chemistry scientists by publication count, Research.com 2026 ranking edition. Based on 17,934 ranked scientists.
Publications Scientists This scientist
61–80 66
81–100 302
101–120 623
121–140 918
141–160 1,218
161–180 1,350
181–200 1,344 182
201–220 1,281
221–240 1,216
241–260 1,100
261–280 979
281–300 939
301–320 764
321–340 643
341–360 628
361–380 522
381–400 459
401–420 397
421–440 327
441–460 270
461–480 265
481–500 252
501–520 201
521–540 185
541–560 148
561–580 148
581–600 132
601–620 114
621–640 104
641–660 91
661–680 92
681–700 73
701–720 57
721–740 54
741–760 67
761–780 45
781–800 46
801–820 39
821–840 32
841–860 36
861–880 29
881–900 26
901–920 24
921–940 14
941–960 23
961–980 28
981–1,000 15
1,001–1,020 29
1,021–1,040 12
1,041–1,060 19
1,061–1,080 12
1,081–1,100 6
1,101–1,120 8
1,121–1,140 12
1,141–1,160 5
1,161–1,180 6
1,181–1,200 14
1,201–1,220 7
1,221–1,240 2
1,241–1,260 6
1,261–1,280 4
1,281–1,294 6
1,295+ 100
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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.

No. of scientists
250 500 750 1,000
Bar chart with 61 bars. Horizontal axis: D-Index, 40–41 to 159+. Vertical axis: number of scientists, 0 to 1,051. Most scientists, 1,051, have 56–57 D-Index. The last bar groups every scientist with 159 D-Index or more. The highlighted bar, 58–59 D-Index, is where this scientist sits. 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
40–41 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.

View D-Index distribution as a table
Number of Chemistry scientists by D-index, Research.com 2026 ranking edition. Based on 17,934 ranked scientists.
D-Index Scientists This scientist
40–41 289
42–43 612
44–45 808
46–47 776
48–49 835
50–51 861
52–53 872
54–55 933
56–57 1,051
58–59 930 59
60–61 882
62–63 834
64–65 731
66–67 775
68–69 683
70–71 646
72–73 561
74–75 501
76–77 437
78–79 388
80–81 354
82–83 292
84–85 275
86–87 254
88–89 235
90–91 185
92–93 192
94–95 155
96–97 163
98–99 125
100–101 105
102–103 105
104–105 112
106–107 88
108–109 68
110–111 69
112–113 65
114–115 79
116–117 61
118–119 44
120–121 37
122–123 40
124–125 33
126–127 26
128–129 34
130–131 35
132–133 25
134–135 27
136–137 17
138–139 16
140–141 20
142–143 20
144–145 15
146–147 9
148–149 9
150–151 16
152–153 11
154–155 9
156–157 3
158 3
159+ 98
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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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