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D-Index
69
Citations
20609
World Ranking
1114
National Ranking
16

Overview

Michele Ceriotti is a researcher affiliated with the École Polytechnique Fédérale de Lausanne in Switzerland. Their academic work focuses primarily on Materials Science, with a significant number of publications in the subfield of Materials Chemistry. Additional areas of study include Computational Theory and Mathematics, Atomic and Molecular Physics and Optics, Biomedical Engineering, and Molecular Biology.

Their research topics cover a broad spectrum within the materials and computational sciences, including:

  • Machine Learning in Materials Science
  • Computational Drug Discovery Methods
  • X-ray Diffraction in Crystallography
  • Protein Structure and Dynamics
  • Advanced Chemical Physics Studies
  • Electron and X-Ray Spectroscopy Techniques
  • Advanced Materials Characterization Techniques

Michele Ceriotti has contributed to numerous scientific publications, appearing frequently in specific venues such as:

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

Their recent published works include:

  • "Gaussian Process Regression for Materials and Molecules," 2021, Chemical Reviews
  • "Origins of structural and electronic transitions in disordered silicon," 2021, Nature
  • "Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems," 2020, arXiv (Cornell University)
  • "Incompleteness of Atomic Structure Representations," 2020, Physical Review Letters
  • "Roadmap on Machine learning in electronic structure," 2022, Electronic Structure

Collaborations are an important aspect of their work, with frequent co-authors including:

  • Guillaume Fraux
  • Jigyasa Nigam
  • Sergey N. Pozdnyakov
  • Federico Grasselli
  • Filippo Bigi

Best Publications

  • Physics-Inspired Structural Representations for Molecules and Materials.

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

  • Comparing molecules and solids across structural and alchemical space.

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

  • Machine learning unifies the modeling of materials and molecules

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

  • Nuclear Quantum Effects in Water and Aqueous Systems: Experiment, Theory, and Current Challenges

    Michele Ceriotti;Wei Fang;Peter G. Kusalik;Ross H. McKenzie

  • Nuclear quantum effects enter the mainstream

    Thomas E. Markland;Michele Ceriotti

  • Efficient stochastic thermostatting of path integral molecular dynamics.

    Michele Ceriotti;Michele Parrinello;Thomas E. Markland;David E. Manolopoulos

  • i-PI 2.0: A universal force engine for advanced molecular simulations

    Venkat Kapil;Mariana Rossi;Ondrej Marsalek;Ondrej Marsalek;Riccardo Petraglia

  • Barely porous organic cages for hydrogen isotope separation.

    Ming Liu;Linda Zhang;Marc A. Little;Venkat Kapil

  • Simplifying the representation of complex free-energy landscapes using sketch-map

    Michele Ceriotti;Gareth A. Tribello;Michele Parrinello

  • Ab initio thermodynamics of liquid and solid water

    Bingqing Cheng;Edgar A Engel;Jörg Behler;Christoph Dellago

  • 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

  • Nuclear quantum effects in solids using a colored-noise thermostat.

    Michele Ceriotti;Giovanni Bussi;Michele Parrinello

  • Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials.

    Giulio Imbalzano;Andrea Anelli;Daniele Giofré;Sinja Klees

  • Colored-Noise Thermostats à la Carte

    Michele Ceriotti;Giovanni Bussi;Michele Parrinello

  • How to remove the spurious resonances from ring polymer molecular dynamics

    Mariana Rossi;Michele Ceriotti;David E. Manolopoulos

  • i-PI: A Python interface for ab initio path integral molecular dynamics simulations

    Michele Ceriotti;Joshua More;David E. Manolopoulos

  • Nuclear quantum effects and hydrogen bond fluctuations in water

    Michele Ceriotti;Jérôme Cuny;Michele Parrinello;David E. Manolopoulos

  • Transferable Machine-Learning Model of the Electron Density

    Andrea Grisafi;Alberto Fabrizio;Benjamin Meyer;David M. Wilkins

  • Chemical shifts in molecular solids by machine learning

    Federico M. Paruzzo;Albert Hofstetter;Félix Musil;Sandip De

  • Langevin Equation with Colored Noise for Constant-Temperature Molecular Dynamics Simulations

    Michele Ceriotti;Giovanni Bussi;Michele Parrinello

  • Ab initio thermodynamics of liquid and solid water: supplemental materials

    Bingqing Cheng;Edgar Engel;Jörg Behler;Christoph Dellago

Frequent Co-Authors

David E. Manolopoulos
David E. Manolopoulos University of Oxford
Gábor Csányi
Gábor Csányi University of Cambridge
Marco Bernasconi
Marco Bernasconi University of Milano-Bicocca
Chris J. Pickard
Chris J. Pickard University of Cambridge
Davide Donadio
Davide Donadio University of California, Davis
Clémence Corminboeuf
Clémence Corminboeuf École Polytechnique Fédérale de Lausanne
Luciano Colombo
Luciano Colombo University of Cagliari
Francesco Paesani
Francesco Paesani University of California, San Diego
Lyndon Emsley
Lyndon Emsley École Polytechnique Fédérale de Lausanne

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