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Navigating molecular networks

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Navigating molecular networks/ by N. Sukumar.
作者:	Sukumar, N.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xviii, 114 p. :ill. (some col.), digital ;24 cm.
內容註:	Molecular Networks -- Transformations of Chemical Space -- Spectral Graph Theory -- Universality and Random Matrix Theory -- Mapping and Navigating Chemical Space Networks -- Generative AI - Growing the Network -- Discovery and Creativity.
Contained By:	Springer Nature eBook
標題:	Molecular theory. -
電子資源:	https://doi.org/10.1007/978-3-031-76290-1
ISBN:	9783031762901

Navigating molecular networks
Sukumar, N.

Navigating molecular networks[electronic resource] /by N. Sukumar. - Cham :Springer Nature Switzerland :2024. - xviii, 114 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in materials,2192-1105. - SpringerBriefs in materials..

Molecular Networks -- Transformations of Chemical Space -- Spectral Graph Theory -- Universality and Random Matrix Theory -- Mapping and Navigating Chemical Space Networks -- Generative AI - Growing the Network -- Discovery and Creativity.

This book delves into the foundational principles governing the treatment of molecular networks and "chemical space"-the comprehensive domain encompassing all physically achievable molecules-from the perspectives of vector space, graph theory, and data science. It explores similarity kernels, network measures, spectral graph theory, and random matrix theory, weaving intriguing connections between these diverse subjects. Notably, it emphasizes the visualization of molecular networks. The exploration continues by delving into contemporary generative deep learning models, increasingly pivotal in the pursuit of new materials possessing specific properties, showcasing some of the most compelling advancements in this field. Concluding with a discussion on the meanings of discovery, creativity, and the role of artificial intelligence (AI) therein. Its primary audience comprises senior undergraduate and graduate students specializing in physics, chemistry, and materials science. Additionally, it caters to those interested in the potential transformation of material discovery through computational, network, AI, and machine learning (ML) methodologies.

ISBN: 9783031762901

Standard No.: 10.1007/978-3-031-76290-1doiSubjects--Topical Terms:

551803
Molecular theory.

LC Class. No.: QD461

Dewey Class. No.: 541.22

Navigating molecular networks
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520 $a This book delves into the foundational principles governing the treatment of molecular networks and "chemical space"-the comprehensive domain encompassing all physically achievable molecules-from the perspectives of vector space, graph theory, and data science. It explores similarity kernels, network measures, spectral graph theory, and random matrix theory, weaving intriguing connections between these diverse subjects. Notably, it emphasizes the visualization of molecular networks. The exploration continues by delving into contemporary generative deep learning models, increasingly pivotal in the pursuit of new materials possessing specific properties, showcasing some of the most compelling advancements in this field. Concluding with a discussion on the meanings of discovery, creativity, and the role of artificial intelligence (AI) therein. Its primary audience comprises senior undergraduate and graduate students specializing in physics, chemistry, and materials science. Additionally, it caters to those interested in the potential transformation of material discovery through computational, network, AI, and machine learning (ML) methodologies.
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