03615nam 22007455 450 991095179870332120250123120020.09783031762901(electronic bk.)978303176289510.1007/978-3-031-76290-1(MiAaPQ)EBC31887303(Au-PeEL)EBL31887303(CKB)37345588100041(DE-He213)978-3-031-76290-1(OCoLC)1492209760(EXLCZ)993734558810004120250123d2024 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierNavigating Molecular Networks /by N. Sukumar1st ed. 2024.Cham :Springer Nature Switzerland :Imprint: Springer,2024.1 online resource (151 pages)SpringerBriefs in Materials,2192-1105Print version: Sukumar, N. Navigating Molecular Networks Cham : Springer,c2025 9783031762895 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.SpringerBriefs in Materials,2192-1105Statistical physicsBiophysicsBiomoleculesGraph theoryStochastic processesMachine learningSoft condensed matterStatistical PhysicsMolecular BiophysicsGraph TheoryStochastic NetworksMachine LearningSoft MaterialsStatistical physics.Biophysics.Biomolecules.Graph theory.Stochastic processes.Machine learning.Soft condensed matter.Statistical Physics.Molecular Biophysics.Graph Theory.Stochastic Networks.Machine Learning.Soft Materials.530.13Sukumar N1806540MiAaPQMiAaPQMiAaPQ9910951798703321Navigating Molecular Networks4355772UNINA