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Titlebook: Machine Learning in Molecular Sciences; Chen Qu,Hanchao Liu Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive lic

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樓主
發(fā)表于 2025-3-21 17:20:40 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Machine Learning in Molecular Sciences
編輯Chen Qu,Hanchao Liu
視頻videohttp://file.papertrans.cn/621/620699/620699.mp4
概述Comprehensive survey of machine learning in molecular sciences.Perspectives on challenges and future of machine learning in chemistry.Features contributions from experts in the field
叢書名稱Challenges and Advances in Computational Chemistry and Physics
圖書封面Titlebook: Machine Learning in Molecular Sciences;  Chen Qu,Hanchao Liu Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive lic
描述Machine learning and artificial intelligence have propelled research across various molecular science disciplines thanks to the rapid progress in computing hardware, algorithms, and data accumulation. This book presents recent machine learning applications in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, to the design and synthesis of large organic and biological molecules. This book is a valuable resource for researchers and students interested in applying machine learning in the research of molecular sciences.
出版日期Book 2023
關(guān)鍵詞Machine Learning; Molecular Sciences; Deep Learning; Artificial Intelligence; Graph Neural Networks; Voxe
版次1
doihttps://doi.org/10.1007/978-3-031-37196-7
isbn_softcover978-3-031-37198-1
isbn_ebook978-3-031-37196-7Series ISSN 2542-4491 Series E-ISSN 2542-4483
issn_series 2542-4491
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 21:11:52 | 只看該作者
Machine Learning in Molecular Sciences978-3-031-37196-7Series ISSN 2542-4491 Series E-ISSN 2542-4483
板凳
發(fā)表于 2025-3-22 02:37:21 | 只看該作者
https://doi.org/10.1007/978-3-031-37196-7Machine Learning; Molecular Sciences; Deep Learning; Artificial Intelligence; Graph Neural Networks; Voxe
地板
發(fā)表于 2025-3-22 07:45:05 | 只看該作者
Development of Exchange-Correlation Functionals Assisted by Machine Learning,ation?functionals of density functional theory. In this chapter, we review how the ML tools are used for this and the performances achieved recently. It is revealed that the ML, not being opposed to the analytical methods, complements human intuition and advances the development of the first-principles calculation with desired accuracy.
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發(fā)表于 2025-3-22 09:34:15 | 只看該作者
Chen Qu,Hanchao LiuComprehensive survey of machine learning in molecular sciences.Perspectives on challenges and future of machine learning in chemistry.Features contributions from experts in the field
6#
發(fā)表于 2025-3-22 15:45:54 | 只看該作者
Challenges and Advances in Computational Chemistry and Physicshttp://image.papertrans.cn/m/image/620699.jpg
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發(fā)表于 2025-3-23 05:52:23 | 只看該作者
Development of Exchange-Correlation Functionals Assisted by Machine Learning,ation?functionals of density functional theory. In this chapter, we review how the ML tools are used for this and the performances achieved recently. It is revealed that the ML, not being opposed to the analytical methods, complements human intuition and advances the development of the first-princip
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