LEADER 00700nam0-22002651i-450- 001 990003492640403321 005 20001010 035 $a000349264 035 $aFED01000349264 035 $a(Aleph)000349264FED01 035 $a000349264 100 $a20000920d1906----km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $a<>"Comune" di Parigi.$fArturo Labriola 210 $aLugano$cs.e.$d1906 700 1$aLabriola,$bArturo$f<1873-1959>$068325 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003492640403321 952 $aSE 014.05.012-$b26602$fDECSE 959 $aDECSE 996 $a"Comune" di Parigi$9496704 997 $aUNINA DB $aING01 LEADER 03615nam 22007455 450 001 9910951798703321 005 20251204110041.0 010 $a9783031762901$b(electronic bk.) 010 $z9783031762895 024 7 $a10.1007/978-3-031-76290-1 035 $a(MiAaPQ)EBC31887303 035 $a(Au-PeEL)EBL31887303 035 $a(CKB)37345588100041 035 $a(DE-He213)978-3-031-76290-1 035 $a(OCoLC)1492209760 035 $a(EXLCZ)9937345588100041 100 $a20250123d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNavigating Molecular Networks /$fby N. Sukumar 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (151 pages) 225 1 $aSpringerBriefs in Materials,$x2192-1105 311 08$aPrint version: Sukumar, N. Navigating Molecular Networks Cham : Springer,c2025 9783031762895 327 $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. 330 $aThis book delves into the foundational principles governing the treatment of molecular networks and "chemical space"?the comprehensive domain encompassing all physically achievable molecules?from the perspectives of vector space, graph theory, and data science. It explores similarity kernels, network measures, spectral graph theory, and random matrix theory, weaving intriguing connections between these diverse subjects. Notably, it emphasizes the visualization of molecular networks. The exploration continues by delving into contemporary generative deep learning models, increasingly pivotal in the pursuit of new materials possessing specific properties, showcasing some of the most compelling advancements in this field. Concluding with a discussion on the meanings of discovery, creativity, and the role of artificial intelligence (AI) therein. Its primary audience comprises senior undergraduate and graduate students specializing in physics, chemistry, and materials science. Additionally, it caters to those interested in the potential transformation of material discovery through computational, network, AI, and machine learning (ML) methodologies. 410 0$aSpringerBriefs in Materials,$x2192-1105 606 $aStatistical physics 606 $aBiophysics 606 $aBiomolecules 606 $aGraph theory 606 $aStochastic processes 606 $aMachine learning 606 $aSoft condensed matter 606 $aStatistical Physics 606 $aMolecular Biophysics 606 $aGraph Theory 606 $aStochastic Networks 606 $aMachine Learning 606 $aSoft Materials 615 0$aStatistical physics. 615 0$aBiophysics. 615 0$aBiomolecules. 615 0$aGraph theory. 615 0$aStochastic processes. 615 0$aMachine learning. 615 0$aSoft condensed matter. 615 14$aStatistical Physics. 615 24$aMolecular Biophysics. 615 24$aGraph Theory. 615 24$aStochastic Networks. 615 24$aMachine Learning. 615 24$aSoft Materials. 676 $a530.13 700 $aSukumar$b N$01806540 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910951798703321 996 $aNavigating Molecular Networks$94355772 997 $aUNINA