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9780198893417

Molecular Dynamics: Probability and Uncertainty

by Coveney, Peter V.; Wan, Shunzhou
  • ISBN13:

    9780198893417

  • ISBN10:

    0198893418

  • eBook ISBN(s):

    9780198893479

  • Additional ISBN(s):

    9780198893479

  • Format: Hardcover
  • Copyright: 2025-08-15
  • Publisher: Oxford University Press
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Author Biography

Peter V. Coveney, Director of the Centre for Computational Science and Professor of Physical Chemistry, University College London,Shunzhou Wan, Senior Research Fellow, Department of Chemistry, University College London

At University College London, Professor Peter V. Coveney holds a chair in Physical Chemistry, is Director of the Centre for Computational Science, is an Associate Director in the Centre of Advanced Research Computing, and is an Honorary Professor in Computer Science. He is a Professor in Applied High Performance Computing and Quantum Computing at the University of Amsterdam and Professor Adjunct at Yale University School of Medicine. Coveney is active in a broad area of interdisciplinary research including computational science, high performance computing, quantum computing, condensed matter physics and chemistry, materials science, and life and medical sciences. He has published more than 500 scientific papers and co-authored three best-selling books. He is a Fellow of the Royal Academy of Engineering and a Member of the Academia Europaea.


Dr Shunzhou Wan is a research fellow in the Centre for Computational Science (CCS) at University College London (UCL). He completed his PhD in Physics at the University of Science and Technology of China (USTC). After his PhD, he worked as a post-doctoral researcher in Martin Karplus's group at Université Louis Pasteur, Strasbourg, France. His research focusses on molecular dynamics simulations and free energy calculations. His recent work involves developing ensemble-based protocols for rapid and accurate free energy calculations, advancing scientific methods for drug discovery through computer simulations integrated with machine learning techniques, and exploring uncertainty quantification in molecular dynamics simulations.

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