Information science materials discovery (23 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2015
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Hardback. Zustand: New. 1st ed. 2016. 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.
Sprache: Englisch
Verlag: Springer, 2019
Serie: Buch 136 von 233 - Springer Series in Materials Science
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Sprache: Englisch
Verlag: Springer, 2015
Serie: Buch 136 von 233 - Springer Series in Materials Science
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Sprache: Englisch
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Serie: Buch 136 von 233 - Springer Series in Materials Science
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Hardback. Zustand: New. 1st ed. 2016.
Sprache: Englisch
Verlag: Springer, 2015
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Weitere BilderSprache: Englisch
Verlag: Springer, 2019
Serie: Buch 136 von 233 - Springer Series in Materials Science
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Taschenbuch. Zustand: Neu. Information Science for Materials Discovery and Design | Turab Lookman (u. a.) | Taschenbuch | Springer Series in Materials Science | xvii | Englisch | 2019 | Springer | EAN 9783319795416 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartman…n[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer International Publishing, Springer International Publishing, 2015
Serie: Buch 136 von 233 - Springer Series in Materials Science
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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 d…ata 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.
Sprache: Englisch
Verlag: Springer, 2019
Serie: Buch 136 von 233 - Springer Series in Materials Science
- Softcover
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Taschenbuch. 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 experimen…ts 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.
Information Science for Materials Discovery and Design
Lookman, Turab (Editor)/ Alexander, Francis J. (Editor)/ Rajan, Krishna (Editor)
Sprache: Englisch
Verlag: Springer Verlag, 2017
Serie: Buch 136 von 233 - Springer Series in Materials Science
- Softcover
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Information Science for Materials Discovery and Design
Lookman, Turab (Edited by)/ Alexander, Francis J. (Edited by)/ Rajan, Krishna (Edited by)
Sprache: Englisch
Verlag: Springer, 2015
Serie: Buch 136 von 233 - Springer Series in Materials Science
- Hardcover
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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.
Sprache: Englisch
Verlag: Springer International Publishing, 2015
Serie: Buch 136 von 233 - Springer Series in Materials Science
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Gebunden. 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.
Sprache: Englisch
Verlag: Springer International Publishing, Springer Nature Switzerland Mär 2019, 2019
Serie: Buch 136 von 233 - Springer Series in Materials Science
- Softcover
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Taschenbuch. 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, combina…torial 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.
Sprache: Englisch
Verlag: Springer International Publishing, Springer Nature Switzerland Dez 2015, 2015
Serie: Buch 136 von 233 - Springer Series in Materials Science
- Hardcover
- Print-on-Demand
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, DeutschlandBuchWeltWeit Ludwig Meier e.K.
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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.
Sprache: Englisch
Verlag: Springer, 2015
Serie: Buch 136 von 233 - Springer Series in Materials Science
- Hardcover
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Sprache: Englisch
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Serie: Buch 136 von 233 - Springer Series in Materials Science
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. 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, combinatori…al 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 328 pp. Englisch.
Sprache: Englisch
Verlag: Springer International Publishing, Springer International Publishing Dez 2015, 2015
Serie: Buch 136 von 233 - Springer Series in Materials Science
- Hardcover
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Buch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. 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 expe…riments 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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 328 pp. Englisch.
Sprache: Englisch
Verlag: Springer, 2015
Serie: Buch 136 von 233 - Springer Series in Materials Science
- Hardcover
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Zustand: New. PRINT ON DEMAND pp. 340.
