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020 _a9783031762901
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024 7 _a10.1007/978-3-031-76290-1
_2doi
050 4 _aQC174.7-175.36
072 7 _aPHS
_2bicssc
072 7 _aSCI040000
_2bisacsh
072 7 _aPHS
_2thema
100 1 _aSukumar, N.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_990397
245 1 0 _aNavigating Molecular Networks
_h[electronic resource] /
_cby N. Sukumar.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXVIII, 114 p. 41 illus., 26 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Materials,
_x2192-1105
505 0 _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.
520 _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.
650 0 _aStatistical Physics.
_970323
650 0 _aBiophysics.
_966239
650 0 _aBiomolecules.
_966771
650 0 _aGraph theory.
650 0 _aStochastic processes.
_976770
650 0 _aMachine learning.
_955059
650 0 _aSoft condensed matter.
_966723
650 1 4 _aStatistical Physics.
_970323
650 2 4 _aMolecular Biophysics.
_966774
650 2 4 _aGraph Theory.
650 2 4 _aStochastic Networks.
_976771
650 2 4 _aMachine Learning.
_955059
650 2 4 _aSoft Materials.
_966725
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031762895
776 0 8 _iPrinted edition:
_z9783031762918
830 0 _aSpringerBriefs in Materials,
_x2192-1105
_967989
856 4 0 _uhttps://doi.org/10.1007/978-3-031-76290-1
912 _aZDB-2-PHA
912 _aZDB-2-SXP
999 _c66487
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