001435495 000__ 04195cam\a2200637\i\4500
001435495 001__ 1435495
001435495 003__ OCoLC
001435495 005__ 20230309003857.0
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001435495 007__ cr\un\nnnunnun
001435495 008__ 210403s2021\\\\sz\a\\\\ob\\\\001\0\eng\d
001435495 019__ $$a1244535201$$a1253412271
001435495 020__ $$a3030683109$$q(electronic book)
001435495 020__ $$a9783030683115$$q(print)
001435495 020__ $$a3030683117
001435495 020__ $$a9783030683122$$q(print)
001435495 020__ $$a3030683125
001435495 020__ $$a9783030683108$$q(electronic bk.)
001435495 020__ $$z9783030683092
001435495 020__ $$z3030683095
001435495 0247_ $$a10.1007/978-3-030-68310-8$$2doi
001435495 035__ $$aSP(OCoLC)1244623672
001435495 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dYDX$$dGW5XE$$dOCLCO$$dOCLCF$$dVT2$$dLIP$$dUKAHL$$dN$T$$dOCLCO$$dOCLCQ$$dCOM$$dOCLCQ
001435495 049__ $$aISEA
001435495 050_4 $$aTA404.23
001435495 08204 $$a620.1/10285$$223
001435495 24500 $$aArtificial intelligence for materials science /$$cYuan Cheng, Tian Wang, Gang Zhang, editors.
001435495 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2021]
001435495 300__ $$a1 online resource (vii, 228 pages) :$$billustrations (some color)
001435495 336__ $$atext$$btxt$$2rdacontent
001435495 337__ $$acomputer$$bc$$2rdamedia
001435495 338__ $$aonline resource$$bcr$$2rdacarrier
001435495 347__ $$atext file
001435495 347__ $$bPDF
001435495 4901_ $$aSpringer series in materials science,$$x0933-033X ;$$vvolume 312
001435495 504__ $$aIncludes bibliographical references and index.
001435495 5050_ $$aBrief Introduction of the Machine Learning Method -- Machine learning for high-entropy alloys -- Two-way TrumpetNets and TubeNets for Identification of Material Parameters -- Machine learning interatomic force fields for carbon allotropic materials -- Genetic Algorithms -- Accelerated Discovery of Thermoelectric Materials using Machine Learning -- Thermal nanostructure design based on materials informatics -- Machine Learning Accelerated Insights of Perovskite Materials.
001435495 506__ $$aAccess limited to authorized users.
001435495 520__ $$aMachine learning methods have lowered the cost of exploring new structures of unknown compounds, and can be used to predict reasonable expectations and subsequently validated by experimental results. As new insights and several elaborative tools have been developed for materials science and engineering in recent years, it is an appropriate time to present a book covering recent progress in this field. Searchable and interactive databases can promote research on emerging materials. Recently, databases containing a large number of high-quality materials properties for new advanced materials discovery have been developed. These approaches are set to make a significant impact on human life and, with numerous commercial developments emerging, will become a major academic topic in the coming years. This authoritative and comprehensive book will be of interest to both existing researchers in this field as well as others in the materials science community who wish to take advantage of these powerful techniques. The book offers a global spread of authors, from USA, Canada, UK, Japan, France, Russia, China and Singapore, who are all world recognized experts in their separate areas. With content relevant to both academic and commercial points of view, and offering an accessible overview of recent progress and potential future directions, the book will interest graduate students, postgraduate researchers, and consultants and industrial engineers.
001435495 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 15, 2021).
001435495 650_0 $$aMaterials science$$xData processing.
001435495 650_0 $$aMachine learning.
001435495 650_6 $$aScience des matériaux$$xInformatique.
001435495 650_6 $$aApprentissage automatique.
001435495 655_0 $$aElectronic books.
001435495 7001_ $$aCheng, Yuan,$$eeditor.
001435495 7001_ $$aWang, Tian,$$eeditor.
001435495 7001_ $$aZhang, Gang,$$eeditor.
001435495 77608 $$iPrint version:$$z9783030683092
001435495 830_0 $$aSpringer series in materials science ;$$vv. 312.$$x0933-033X
001435495 852__ $$bebk
001435495 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-68310-8$$zOnline Access$$91397441.1
001435495 909CO $$ooai:library.usi.edu:1435495$$pGLOBAL_SET
001435495 980__ $$aBIB
001435495 980__ $$aEBOOK
001435495 982__ $$aEbook
001435495 983__ $$aOnline
001435495 994__ $$a92$$bISE