03139nam a22005295i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001900153072001600172072002300188072001500211100007300226245007500299250001800374264006900392300006700461336002600528337002600554338003600580347002400616490004400640505024600684520116900930650002502099650001602124650001802140650001802158650002602176650002202202650002702224650002502251650002602276650001802302650002502320650002202345650002002367710003402387773002602421776003602447776003602483830004402519856004602563978-3-031-76290-1DE-He21320251030114929.0cr nn 008mamaa250123s2024    sz |    s    |||| 0|eng d  a97830317629017 a10.1007/978-3-031-76290-12doi 4aQC174.7-175.36 7aPHS2bicssc 7aSCI0400002bisacsh 7aPHS2thema1 aSukumar, N.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aNavigating Molecular Networksh[electronic resource] /cby N. Sukumar.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXVIII, 114 p. 41 illus., 26 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSpringerBriefs in Materials,x2192-11050 aMolecular 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.  aThis 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. 0aStatistical Physics. 0aBiophysics. 0aBiomolecules. 0aGraph theory. 0aStochastic processes. 0aMachine learning. 0aSoft condensed matter.14aStatistical Physics.24aMolecular Biophysics.24aGraph Theory.24aStochastic Networks.24aMachine Learning.24aSoft Materials.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303176289508iPrinted edition:z9783031762918 0aSpringerBriefs in Materials,x2192-110540uhttps://doi.org/10.1007/978-3-031-76290-1