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        Chapter 4 Enhanced Numerical Schemes in IMF for Transition States

        Proposal review

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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
        9781003605652, 9781032996479, 9781032997186
        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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