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Añadir al carritoPaperback. Condición: Brand New. 100 pages. 9.26x6.11x9.21 inches. In Stock.
Idioma: Inglés
Publicado por Springer Nature Switzerland, Springer Nature Switzerland, 2025
ISBN 10: 3031762894 ISBN 13: 9783031762895
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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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Añadir al carritoTaschenbuch. Condición: Neu. Navigating Molecular Networks | N. Sukumar | Taschenbuch | SpringerBriefs in Materials | xviii | Englisch | 2025 | Springer | EAN 9783031762895 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Publicado por Springer, Berlin, Springer Nature Switzerland, Springer, 2025
ISBN 10: 3031762894 ISBN 13: 9783031762895
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 114 pp. Englisch.
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
Idioma: Inglés
Publicado por Springer, Palgrave Macmillan Jan 2025, 2025
ISBN 10: 3031762894 ISBN 13: 9783031762895
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 53,49
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 132 pp. Englisch.