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O. Anatole von Lilienfeld

O. Anatole von Lilienfeld

D-Index & Metrics

Chemistry

D-Index
59
Citations
18774
World Ranking
9997
National Ranking
265

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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