LEADER 01180cam0-2200409---450- 001 990004242260403321 005 20101022095253.0 010 $a88-339-0942-5 035 $a000424226 035 $aFED01000424226 035 $a(Aleph)000424226FED01 035 $a000424226 100 $a19990604d1995----km-y0itay50------ba 101 1 $aita$ceng 102 $aIT 105 $ay-------001yy 200 1 $aChe cos'è il nazismo?$eproblemi interpretativi e prospettive di ricerca$fIan Kershaw 210 $aTorino$cBollati Boringhieri$d1995 215 $a362 p.$d22 cm 225 1 $aNuova cultura$v49 454 0$12001$a<> Nazi dictatorship$918904 610 0 $aNazionalsocialismo$aStoriografia 676 $a943.086 700 1$aKershaw,$bIan$f<1943- >$0142945 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990004242260403321 952 $a320.5330943 KER 1$b5346$fBFS 952 $aN05.790$b10065$fDECTS 952 $aCOLLEZ. 1164 (49)$b27190$fFSPBC 952 $a943.086 KER 3$bBibl.25677$fFLFBC 959 $aDECTS 959 $aBFS 959 $aFLFBC 959 $aFSPBC 996 $aNazi dictatorship$918904 997 $aUNINA LEADER 05371 am 22008893u 450 001 9910293141503321 005 20230125214013.0 010 $a981-10-7617-0 024 7 $a10.1007/978-981-10-7617-6 035 $a(CKB)4100000001795164 035 $a(DE-He213)978-981-10-7617-6 035 $a(MiAaPQ)EBC5578446 035 $a(Au-PeEL)EBL5578446 035 $a(OCoLC)1065413852 035 $a(MiAaPQ)EBC6422634 035 $a(Au-PeEL)EBL6422634 035 $a(OCoLC)1021203347 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/46733 035 $a(PPN)223954314 035 $a(EXLCZ)994100000001795164 100 $a20180115d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNanoinformatics$b[electronic resource] /$fedited by Isao Tanaka 205 $a1st ed. 2018. 210 $cSpringer Nature$d2018 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2018. 215 $a1 online resource (VIII, 298 p. 188 illus., 142 illus. in color.) 311 $a981-10-7616-2 327 $a1. Descriptors for Machine Learning of Materials Data -- 2. Potential Energy Surface Mapping of Charge Carriers in Ionic Conductors Based on a Gaussian Process Model -- 3. Machine learning predictions of factors affecting the activity of heterogeneous metal catalysts -- 4. Machine Learning-based Experimental Design in Materials Science -- 5. Persistent homology and materials informatics -- 6. Polyhedron and Polychoron codes for describing Atomic Arrangements -- 7. Topological Data Analysis for the Characterization of Atomic Scale Morphology from Atom Probe Tomography Images -- 8. Atomic-scale nanostructures by advanced electron microscopy and informatics -- 9. High spatial resolution hyperspectral imaging with machine-learning techniques -- 10. Fabrication, Characterization, and Modulation of Functional Nanolayers -- 11. Grain Boundary Engineering of Alumina Ceramics -- 12. Structural relaxation of oxide compounds from the high-pressure phase.-13.Synthesis and structures of novel solid-state electrolytes. 330 $aThis open access book brings out the state of the art on how informatics-based tools are used and expected to be used in nanomaterials research. There has been great progress in the area in which ?big-data? generated by experiments or computations are fully utilized to accelerate discovery of new materials, key factors, and design rules. Data-intensive approaches play indispensable roles in advanced materials characterization. "Materials informatics" is the central paradigm in the new trend. "Nanoinformatics" is its essential subset, which focuses on nanostructures of materials such as surfaces, interfaces, dopants, and point defects, playing a critical role in determining materials properties. There have been significant advances in experimental and computational techniques to characterize individual atoms in nanostructures and to gain quantitative information. The collaboration of researchers in materials science and information science is growing actively and is creating a new trend in materials science and engineering. This book is open access under a CC BY license. 606 $aNanotechnology 606 $aChemistry, Physical and theoretical 606 $aNanoscale science 606 $aNanoscience 606 $aNanostructures 606 $aMaterials science 606 $aSpectroscopy 606 $aMicroscopy 606 $aNanotechnology$3https://scigraph.springernature.com/ontologies/product-market-codes/Z14000 606 $aTheoretical and Computational Chemistry$3https://scigraph.springernature.com/ontologies/product-market-codes/C25007 606 $aNanoscale Science and Technology$3https://scigraph.springernature.com/ontologies/product-market-codes/P25140 606 $aCharacterization and Evaluation of Materials$3https://scigraph.springernature.com/ontologies/product-market-codes/Z17000 606 $aSpectroscopy/Spectrometry$3https://scigraph.springernature.com/ontologies/product-market-codes/C11020 606 $aSpectroscopy and Microscopy$3https://scigraph.springernature.com/ontologies/product-market-codes/P31090 610 $aFirst-principles calculations 610 $aNanomaterials synthesis 610 $aMachine learning 610 $aBig data 610 $aAtomic resolution characterization 615 0$aNanotechnology. 615 0$aChemistry, Physical and theoretical. 615 0$aNanoscale science. 615 0$aNanoscience. 615 0$aNanostructures. 615 0$aMaterials science. 615 0$aSpectroscopy. 615 0$aMicroscopy. 615 14$aNanotechnology. 615 24$aTheoretical and Computational Chemistry. 615 24$aNanoscale Science and Technology. 615 24$aCharacterization and Evaluation of Materials. 615 24$aSpectroscopy/Spectrometry. 615 24$aSpectroscopy and Microscopy. 676 $a620.115 700 $aIsao Tanaka$4auth$01356495 702 $aTanaka$b Isao$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910293141503321 996 $aNanoinformatics$93361078 997 $aUNINA