03536nam a22006135i 4500 978-3-031-76290-1 DE-He213 20251030114929.0 cr nn 008mamaa 250123s2024 sz | s |||| 0|eng d 9783031762901 978-3-031-76290-1 10.1007/978-3-031-76290-1 doi QC174.7-175.36 PHS bicssc SCI040000 bisacsh PHS thema Sukumar, N. author. aut http://id.loc.gov/vocabulary/relators/aut 90397 Navigating Molecular Networks [electronic resource] / by N. Sukumar. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XVIII, 114 p. 41 illus., 26 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda SpringerBriefs in Materials, 2192-1105 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. Statistical Physics. 70323 Biophysics. 66239 Biomolecules. 66771 Graph theory. Stochastic processes. 76770 Machine learning. 55059 Soft condensed matter. 66723 Statistical Physics. 70323 Molecular Biophysics. 66774 Graph Theory. Stochastic Networks. 76771 Machine Learning. 55059 Soft Materials. 66725 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031762895 Printed edition: 9783031762918 SpringerBriefs in Materials, 2192-1105 67989 https://doi.org/10.1007/978-3-031-76290-1 ZDB-2-PHA ZDB-2-SXP 66487 66487