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dc.contributor.editorGu, Shuting
dc.date.accessioned2025-04-29T12:43:25Z
dc.date.available2025-04-29T12:43:25Z
dc.date.issued2025
dc.identifier.urihttps://0-library-oapen-org.catalogue.libraries.london.ac.uk/handle/20.500.12657/101207
dc.description.abstractBased 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://0-www-taylorfrancis-com.catalogue.libraries.london.ac.uk under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.en_US
dc.languageEnglishen_US
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematicsen_US
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWL Stochasticsen_US
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PN Chemistryen_US
dc.subject.otherRare Events Simulation,Computational Science,Stochastic Modeling,Computational Physicsen_US
dc.titleChapter 3 Variants of Gentlest Ascent Dynamics for Transition Statesen_US
dc.typechapter
oapen.identifier.doi10.1201/9781003605652-3en_US
oapen.relation.isPublishedBy7b3c7b10-5b1e-40b3-860e-c6dd5197f0bben_US
oapen.relation.isPartOfBook6e7504d2-5dff-418a-9011-0a2cfbd80bd9en_US
oapen.relation.isFundedBy219cc0eb-31a9-46a1-a50f-c2d756c7fec1en_US
oapen.relation.isFundedBy92342a06-082a-4b07-ac71-5f361d47344aen_US
oapen.relation.isbn9781032996479en_US
oapen.relation.isbn9781032997186en_US
oapen.imprintCRC Pressen_US
oapen.pages33en_US
oapen.grant.number11901211
oapen.remark.publicFunder name: The Natural Science Foundation of Top Talent of SZTU GDRC202137


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