World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
30
Citations
10974
World Ranking
13820
National Ranking
16

Overview

Matthias Rupp is affiliated with the Luxembourg Institute of Science and Technology in Luxembourg. Their research primarily focuses on Materials Science, with significant contributions in Materials Chemistry, Computational Theory and Mathematics, Atomic and Molecular Physics and Optics, Artificial Intelligence, and Electrical and Electronic Engineering.

Their work spans several key topics within these fields, including:

  • Machine Learning in Materials Science
  • Computational Drug Discovery Methods
  • X-ray Diffraction in Crystallography
  • Quantum, superfluid, helium dynamics
  • Electronic and Structural Properties of Oxides
  • Fuel Cells and Related Materials
  • Nuclear Materials and Properties

Matthias Rupp has contributed numerous publications to a variety of scientific venues. The most frequent publication outlets include:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • npj Computational Materials
  • The Journal of Chemical Physics
  • Physical Review B

Among the recent papers authored or co-authored by Matthias Rupp are:

  • "Unified representation of molecules and crystals for machine learning," 2022, Machine Learning Science and Technology
  • "Identifying domains of applicability of machine learning models for materials science," 2020, Nature Communications
  • "Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning," 2022, npj Computational Materials
  • "Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization," 2020, The Journal of Chemical Physics
  • "Ultra-fast interpretable machine-learning potentials," 2023, npj Computational Materials

Their research collaborations frequently involve:

  • Matthias Scheffler
  • Marcel F. Langer
  • Bastian Jäckl
  • Florian Knoop
  • Christian Carbogno

Best Publications

  • Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

    Matthias Rupp;Matthias Rupp;Alexandre Tkatchenko;Alexandre Tkatchenko;Klaus Robert Müller;Klaus Robert Müller;O. Anatole Von Lilienfeld;O. Anatole Von Lilienfeld

  • Quantum chemistry structures and properties of 134 kilo molecules

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

  • Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.

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

  • Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

    Iurii Sushko;Sergii Novotarskyi;Robert Körner;Anil Kumar Pandey

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

    Katja Hansen;Grégoire Montavon;Franziska Biegler;Siamac Fazli

  • Finding density functionals with machine learning.

    John C. Snyder;Matthias Rupp;Katja Hansen;Klaus Robert Müller;Klaus Robert Müller

  • Machine Learning of Molecular Electronic Properties in Chemical Compound Space

    Grégoire Montavon;Matthias Rupp;Vivekanand Gobre;Alvaro Vazquez-Mayagoitia

  • Machine learning for quantum mechanics in a nutshell

    Matthias Rupp;Matthias Rupp

  • DOGS: reaction-driven de novo design of bioactive compounds

    Markus Hartenfeller;Heiko Zettl;Miriam Walter;Matthias Rupp

  • 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

  • Understanding machine‐learned density functionals

    Li Li;John C. Snyder;John C. Snyder;Isabelle M. Pelaschier;Isabelle M. Pelaschier;Jessica Huang

  • Learning Invariant Representations of Molecules for Atomization Energy Prediction

    Grégoire Montavon;Katja Hansen;Siamac Fazli;Matthias Rupp

  • Machine-learned multi-system surrogate models for materials prediction

    Chandramouli Nyshadham;Matthias Rupp;Brayden Bekker;Alexander V. Shapeev

  • Orbital-free bond breaking via machine learning

    John C. Snyder;Matthias Rupp;Matthias Rupp;Katja Hansen;Leo Blooston

  • Identifying domains of applicability of machine learning models for materials science.

    Christopher A. Sutton;Mario Boley;Luca M. Ghiringhelli;Matthias Rupp;Matthias Rupp

  • Unified Representation of Molecules and Crystals for Machine Learning

    Haoyan Huo;Matthias Rupp

  • Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

    Marcel Florin Langer;Alex Goeßmann;Matthias Rupp

  • Optimizing transition states via kernel-based machine learning

    Zachary D. Pozun;Zachary D. Pozun;Katja Hansen;Katja Hansen;Daniel Sheppard;Daniel Sheppard;Matthias Rupp;Matthias Rupp

  • Understanding kernel ridge regression: Common behaviors from simple functions to density functionals

    Kevin Vu;John C. Snyder;John C. Snyder;Li Li;Matthias Rupp

  • Kernel approach to molecular similarity based on iterative graph similarity.

    Matthias Rupp;Ewgenij Proschak;Gisbert Schneider

  • Machine Learning of Molecular Electronic Properties in Chemical Compound Space

    Grégoire Montavon;Matthias Rupp;Vivekanand Gobre;Alvaro Vazquez-Mayagoitia

  • Guest Editorial: Special Topic on Data-enabled Theoretical Chemistry

    Matthias Rupp;O. Anatole von Lilienfeld;Kieron Burke

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

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

  • Finding density functionals with machine learning

    John Snyder;Matthias Rupp;Katja Hansen;Klaus Mueller

Frequent Co-Authors

Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Alexandre Tkatchenko
Alexandre Tkatchenko University of Luxembourg
Kieron Burke
Kieron Burke University of California, Irvine
Matthias Scheffler
Matthias Scheffler Fritz Haber Institute of the Max Planck Society
Igor V. Tetko
Igor V. Tetko Helmholtz Zentrum München
Grégoire Montavon
Grégoire Montavon Freie Universität Berlin
Antony J. Williams
Antony J. Williams Environmental Protection Agency
Jilles Vreeken
Jilles Vreeken Max Planck Society
Johann Gasteiger
Johann Gasteiger University of Erlangen-Nuremberg

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