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Titlebook: Electron Localization-Delocalization Matrices ; Chérif F. Matta,Paul W. Ayers,Ronald Cook Book 2024 Springer Nature Switzerland AG 2024 At

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發(fā)表于 2025-3-21 17:08:16 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Electron Localization-Delocalization Matrices
編輯Chérif F. Matta,Paul W. Ayers,Ronald Cook
視頻videohttp://file.papertrans.cn/307/306120/306120.mp4
概述Offers a clear link between theory/abstract concepts and numerical practical results.Introduces robust statistical modeling to predict molecular properties.Demonstrates the high efficacy of LDMs in va
叢書(shū)名稱Lecture Notes in Chemistry
圖書(shū)封面Titlebook: Electron Localization-Delocalization Matrices ;  Chérif F. Matta,Paul W. Ayers,Ronald Cook Book 2024 Springer Nature Switzerland AG 2024 At
描述This book builds bridges between two yet separated branches of theoretical and mathematical chemistry: Chemical Graph Theory and Electronic Structure Calculations. Although either of the fields have developed their own techniques, problems, methods, and favorite benchmark cases independent from each other, the authors have managed to bring them together by using the localization-delocalization matrix (LDM). The LDM is a novel molecular descriptor that fingerprints a molecule by condensing the complicated electronic information in one, mathematically manageable, object. In this book, the authors introduce the readers to modeling techniques based on LDMs. Their technique offers a high accuracy as well as robust predictive power, often dramatically surpassing the potential of either of the constituting methods on their own.?In addition to the comprehensive and accessible introduction to this new field of theoretical chemistry, the authors offer their self-developed software free to download, so that readers can try running their own simulations. The described methods are very general and can easily be implemented for calculating various properties and parameters such as mosquito repel
出版日期Book 2024
關(guān)鍵詞Atoms in molecules; Chemical graph theory; Electronic structure theory; Matrix representation of molecu
版次1
doihttps://doi.org/10.1007/978-3-031-51434-0
isbn_ebook978-3-031-51434-0Series ISSN 0342-4901 Series E-ISSN 2192-6603
issn_series 0342-4901
copyrightSpringer Nature Switzerland AG 2024
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沙發(fā)
發(fā)表于 2025-3-21 21:24:03 | 只看該作者
0342-4901 ular properties.Demonstrates the high efficacy of LDMs in vaThis book builds bridges between two yet separated branches of theoretical and mathematical chemistry: Chemical Graph Theory and Electronic Structure Calculations. Although either of the fields have developed their own techniques, problems,
板凳
發(fā)表于 2025-3-22 01:08:44 | 只看該作者
https://doi.org/10.1007/978-3-031-55064-5orthogonal, in PCA; or less correlated in the case of FA. If the original data set contains uncorrelated random variables, then PCA and FA become irrelevant since there is no data reduction possible in this case.
地板
發(fā)表于 2025-3-22 07:34:57 | 只看該作者
Principal Component Analysis of Localization-Delocalization Matrices,orthogonal, in PCA; or less correlated in the case of FA. If the original data set contains uncorrelated random variables, then PCA and FA become irrelevant since there is no data reduction possible in this case.
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發(fā)表于 2025-3-22 12:52:34 | 只看該作者
Electron Localization-Delocalization Matrices 978-3-031-51434-0Series ISSN 0342-4901 Series E-ISSN 2192-6603
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發(fā)表于 2025-3-22 20:09:48 | 只看該作者
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發(fā)表于 2025-3-22 22:39:26 | 只看該作者
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發(fā)表于 2025-3-23 03:41:43 | 只看該作者
https://doi.org/10.1007/978-3-030-82483-9Ismat Sumar of Saint Mary’s University, one of whom this book is dedicated, pioneered the first software implementation of an algorithm to automate the localization-delocalization matrices (LDMs) analysis of a series of molecules.
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發(fā)表于 2025-3-23 06:15:37 | 只看該作者
https://doi.org/10.1007/978-3-030-19163-4In previous chapters, we have demonstrated the extraction of chemical information from molecular structures . the use of Bader’s Quantum Theory of Atoms in Molecules (QTAIM) followed by the analysis of the electron localization-delocalization matrices by such methods as Principal Component Analysis (PCA) to predict useful properties.
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