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    Chapter 4 Enhanced Numerical Schemes in IMF for Transition States

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    Contributor(s)
    Gu, Shuting (editor)
    Language
    English
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    Abstract
    Based on the calculation of transition states and the identification of transition paths, this book aims to provide a comprehensive guide to understanding and simulating rare events. The author introduces both fundamental concepts of transition states and pathways and advanced computational techniques, focusing on Gentlest Ascent Dynamics (GAD) and its variants. In particular, she explores enhanced numerical methods such as the convex splitting method and the Scalar Auxiliary Variable (SAV) approach within the Iterative Minimization Formulation (IMF). In addition, the book applies these methods to real-world problems, highlighting the string method and the geometric Minimum Action Method (gMAM) for computing transition paths. The book is written for researchers and practitioners in fields such as applied mathematics, physics, chemistry, and computational science who are interested in the underlying mechanisms of rare events and their transition processes. Chapters 3 and 4 of this book are each freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
    Book
    Computational Methods for Transition States and Pathways in Rare Events
    URI
    https://library.oapen.org/handle/20.500.12657/101208
    Keywords
    Rare Events Simulation,Computational Science,Stochastic Modeling,Computational Physics
    DOI
    10.1201/9781003605652-4
    ISBN
    9781032996479, 9781032997186, 9781003605652
    Publisher
    Taylor & Francis
    Publisher website
    https://taylorandfrancis.com/
    Publication date and place
    2025
    Grantor
    • National Natural Science Foundation of China - 11901211
    • Shenzhen Technology University
    Imprint
    CRC Press
    Classification
    Applied mathematics
    Stochastics
    Chemistry
    Pages
    53
    Public remark
    Funder name: The Natural Science Foundation of Top Talent of SZTU GDRC202137
    Rights
    https://creativecommons.org/licenses/by-nc-nd/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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