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Alle Exemplare der Ausgabe mit dieser ISBN anzeigen:This 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.
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Buchbeschreibung Zustand: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. Bestandsnummer des Verkäufers ria9783319238708_lsuk
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Buchbeschreibung Zustand: New. Bestandsnummer des Verkäufers 24373583-n
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Buchbeschreibung Zustand: New. Bestandsnummer des Verkäufers 24373583-n
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Buchbeschreibung Hardcover. Zustand: new. Bestandsnummer des Verkäufers 9783319238708
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Buchbeschreibung Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. One of the first books on materials discovery strategyEmphasizes the paradigm of codesignBrings together diverse expertise to improve the model for materials discoveryThis book deals with an information-driven approach to plan ma. Bestandsnummer des Verkäufers 43799490
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Buchbeschreibung Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This 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'' toour toolkit to make the 'Materials Genome'' a reality, the science of Materials Informatics. 328 pp. Englisch. Bestandsnummer des Verkäufers 9783319238708
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Buchbeschreibung Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This 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'' toour toolkit to make the 'Materials Genome'' a reality, the science of Materials Informatics. Bestandsnummer des Verkäufers 9783319238708
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Buchbeschreibung Zustand: New. pp. 340. Bestandsnummer des Verkäufers 26372723081
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Buchbeschreibung Zustand: New. Bestandsnummer des Verkäufers I-9783319238708
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Buchbeschreibung Zustand: New. Editor(s): Lookman, Turab; Alexander, Francis J.; Rajan, Krishna. Series: Springer Series in Materials Science. Num Pages: 324 pages, 46 black & white illustrations, 88 colour illustrations, biography. BIC Classification: TGM; UY. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 19. Weight in Grams: 654. . 2015. 1st ed. 2016. hardcover. . . . . Bestandsnummer des Verkäufers V9783319238708
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