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Chemistry

D-Index
72
Citations
28054
World Ranking
5151
National Ranking
300

Overview

Gábor Csányi is affiliated with the University of Cambridge in the United Kingdom and works primarily in the field of Materials Science. Their research encompasses several focused subfields including Materials Chemistry, Computational Theory and Mathematics, Atomic and Molecular Physics and Optics, Molecular Biology, and Electrical and Electronic Engineering.

Their notable research topics include Machine Learning in Materials Science, Computational Drug Discovery Methods, X-ray Diffraction in Crystallography, Protein Structure and Dynamics, Advanced Chemical Physics Studies, Electronic and Structural Properties of Oxides, and Crystallography and Molecular Interactions.

The scientist has contributed extensively to academic literature with frequent publications in several venues. The key publication venues are:

  • arXiv (Cornell University)
  • The Journal of Chemical Physics
  • Zenodo (CERN European Organization for Nuclear Research)
  • npj Computational Materials
  • Journal of Chemical Theory and Computation

Selected recent papers include:

  • Gaussian Process Regression for Materials and Molecules, 2021, Chemical Reviews
  • Performance and Cost Assessment of Machine Learning Interatomic Potentials, 2020, The Journal of Physical Chemistry A
  • Origins of structural and electronic transitions in disordered silicon, 2021, Nature
  • An accurate and transferable machine learning potential for carbon, 2020, Oxford University Research Archive (University of Oxford)
  • Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon, 2021, npj Computational Materials

Frequent co-authors working alongside Gábor Csányi include Volker L. Deringer, Christoph Ortner, Noam Bernstein, Albert P. Bartók, and Michele Ceriotti.

Best Publications

  • Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.

    Albert P. Bartók;Mike C. Payne;Risi Kondor;Gábor Csányi

  • On representing chemical environments

    Albert P. Bartók;Risi Kondor;Gábor Csányi

  • Gaussian Process Regression for Materials and Molecules.

    Volker L. Deringer;Albert P. Bartók;Noam Bernstein;David M. Wilkins

  • Performance and Cost Assessment of Machine Learning Interatomic Potentials.

    Yunxing Zuo;Chi Chen;Xiangguo Li;Zhi Deng

  • Machine Learning Interatomic Potentials as Emerging Tools for Materials Science.

    Volker L. Deringer;Miguel A. Caro;Gábor Csányi

  • Physics-Inspired Structural Representations for Molecules and Materials.

    Felix Musil;Andrea Grisafi;Albert P. Bartók;Christoph Ortner

  • Gaussian approximation potentials: A brief tutorial introduction

    Albert P. Bartók;Gábor Csányi

  • Comparing molecules and solids across structural and alchemical space.

    Sandip De;Albert P. Bartók;Gábor Csányi;Michele Ceriotti

  • Reinforcement of single-walled carbon nanotube bundles by intertube bridging

    A. Kis;G. Csányi;J.-P. Salvetat;Thien-Nga Lee

  • Machine learning unifies the modeling of materials and molecules

    Albert P. Bartók;Sandip De;Carl Poelking;Noam Bernstein

  • Machine learning based interatomic potential for amorphous carbon

    Volker L. Deringer;Gábor Csányi

  • Edge-functionalized and substitutionally doped graphene nanoribbons: Electronic and spin properties

    F. Cervantes-Sodi;G. Csányi;S. Piscanec;A. C. Ferrari

  • Surface diffusion: the low activation energy path for nanotube growth.

    S. Hofmann;G. Csányi;A. C. Ferrari;M. C. Payne

  • Modeling Molecular Interactions in Water: From Pairwise to Many-Body Potential Energy Functions.

    Gerardo Andrés Cisneros;Kjartan Thor Wikfeldt;Lars Ojamäe;Jibao Lu

  • An accurate and transferable machine learning potential for carbon.

    Patrick Rowe;Volker L Deringer;Piero Gasparotto;Gábor Csányi

  • The role of the interlayer state in the electronic structure of superconducting graphite intercalated compounds

    Gábor Csányi;P. B. Littlewood;Andriy H. Nevidomskyy;Chris J. Pickard

  • "Learn on the fly": a hybrid classical and quantum-mechanical molecular dynamics simulation.

    Gabor Csányi;T. Albaret;M. C. Payne;A. De Vita;A. De Vita

  • Accuracy and transferability of Gaussian approximation potential models for tungsten

    Wojciech J. Szlachta;Albert P. Bartók;Gábor Csányi

  • Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

    Andrea Grisafi;David M. Wilkins;Gábor Csányi;Michele Ceriotti

  • Origins of structural and electronic transitions in disordered silicon

    Volker L. Deringer;Noam Bernstein;Gábor Csányi;Chiheb Ben Mahmoud

  • Chemically active substitutional nitrogen impurity in carbon nanotubes.

    Andriy H. Nevidomskyy;Gábor Csányi;Michael C. Payne

  • Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties

    Olga Obrezanova;Gábor Csányi;Joelle M. R. Gola;Matthew D. Segall

Frequent Co-Authors

Mike C. Payne
Mike C. Payne University of Cambridge
Christoph Ortner
Christoph Ortner University of Warwick
Michele Ceriotti
Michele Ceriotti École Polytechnique Fédérale de Lausanne
Stephen R. Elliott
Stephen R. Elliott University of Oxford
Chris J. Pickard
Chris J. Pickard University of Cambridge
Andrea C. Ferrari
Andrea C. Ferrari University of Cambridge
Karsten Reuter
Karsten Reuter Fritz Haber Institute of the Max Planck Society
Davide M. Proserpio
Davide M. Proserpio University of Milan
Stephan Hofmann
Stephan Hofmann University of Cambridge
Michael Moseler
Michael Moseler University of Freiburg

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