LEADER 05624nam 22008295 450 001 9910254043203321 005 20200629115934.0 010 $a3-319-23871-X 024 7 $a10.1007/978-3-319-23871-5 035 $a(CKB)3710000000532695 035 $a(EBL)4199780 035 $a(SSID)ssj0001596994 035 $a(PQKBManifestationID)16297971 035 $a(PQKBTitleCode)TC0001596994 035 $a(PQKBWorkID)14886413 035 $a(PQKB)11754465 035 $a(DE-He213)978-3-319-23871-5 035 $a(MiAaPQ)EBC4199780 035 $a(PPN)190885289 035 $a(EXLCZ)993710000000532695 100 $a20151212d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aInformation Science for Materials Discovery and Design /$fedited by Turab Lookman, Francis J. Alexander, Krishna Rajan 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (316 p.) 225 1 $aSpringer Series in Materials Science,$x0933-033X ;$v225 300 $aDescription based upon print version of record. 311 $a3-319-23870-1 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aFrom the Contents: Introduction -- Data-Driven Discovery of Physical, Chemical, and Pharmaceutical Materials -- Cross-Validation and Inference in Bioinformatics/Cancer Genomics -- Applying MQSPRs - New Challenges and Opportunities. 330 $aThis book deals with an information-driven approach to plan materials discovery and design, iterative learning. The authors present contrasting but complementary approaches, such as those based on high throughput calculations, combinatorial experiments or data driven discovery, together with machine-learning methods. Similarly, statistical methods successfully applied in other fields, such as biosciences, are presented. The content spans from materials science to information science to reflect the cross-disciplinary nature of the field. A perspective is presented that offers a paradigm (codesign loop for materials design) to involve iteratively learning from experiments and calculations to develop materials with optimum properties. Such a loop requires the elements of incorporating domain materials knowledge, a database of descriptors (the genes), a surrogate or statistical model developed to predict a given property with uncertainties, performing adaptive experimental design to guide the next experiment or calculation and aspects of high throughput calculations as well as experiments. The book is about manufacturing with the aim to halving the time to discover and design new materials. Accelerating discovery relies on using large databases, computation, and mathematics in the material sciences in a manner similar to the way used to in the Human Genome Initiative. Novel approaches are therefore called to explore the enormous phase space presented by complex materials and processes. To achieve the desired performance gains, a predictive capability is needed to guide experiments and computations in the most fruitful directions by reducing not successful trials. Despite advances in computation and experimental techniques, generating vast arrays of data; without a clear way of linkage to models, the full value of data driven discovery cannot be realized. Hence, along with experimental, theoretical and computational materials science, we need to add a ?fourth leg?? to our toolkit to make the ?Materials Genome'' a reality, the science of Materials Informatics. 410 0$aSpringer Series in Materials Science,$x0933-033X ;$v225 606 $aNanotechnology 606 $aEngineering?Materials 606 $aData mining 606 $aStatistical physics 606 $aDynamical systems 606 $aMaterials science 606 $aNanotechnology$3https://scigraph.springernature.com/ontologies/product-market-codes/Z14000 606 $aMaterials Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T28000 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aComplex Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/P33000 606 $aCharacterization and Evaluation of Materials$3https://scigraph.springernature.com/ontologies/product-market-codes/Z17000 606 $aStatistical Physics and Dynamical Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/P19090 615 0$aNanotechnology. 615 0$aEngineering?Materials. 615 0$aData mining. 615 0$aStatistical physics. 615 0$aDynamical systems. 615 0$aMaterials science. 615 14$aNanotechnology. 615 24$aMaterials Engineering. 615 24$aData Mining and Knowledge Discovery. 615 24$aComplex Systems. 615 24$aCharacterization and Evaluation of Materials. 615 24$aStatistical Physics and Dynamical Systems. 676 $a620.11 702 $aLookman$b Turab$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aAlexander$b Francis J$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRajan$b Krishna$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254043203321 996 $aInformation Science for Materials Discovery and Design$92541135 997 $aUNINA