Information science materials discovery (23 risultati)
Lingua: Inglese
Editore: Springer, 2015
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Lingua: Inglese
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Lingua: Inglese
Editore: Springer, 2019
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Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2015
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Hardback. Condizione: 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 w…ith 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.
Lingua: Inglese
Editore: Springer, 2019
Serie: Libro 136 di 233 - Springer Series in Materials Science
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Lingua: Inglese
Editore: Springer, 2015
Serie: Libro 136 di 233 - Springer Series in Materials Science
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Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2015
Serie: Libro 136 di 233 - Springer Series in Materials Science
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Hardback. Condizione: New. 1st ed. 2016.
Lingua: Inglese
Editore: Springer, 2015
Serie: Libro 136 di 233 - Springer Series in Materials Science
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Condizione: New. pp. 340.
Altre immaginiLingua: Inglese
Editore: Springer, 2019
Serie: Libro 136 di 233 - Springer Series in Materials Science
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Taschenbuch. Condizione: 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]hart…mann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing, 2015
Serie: Libro 136 di 233 - Springer Series in Materials Science
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: 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 o…r 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.
Lingua: Inglese
Editore: Springer, 2019
Serie: Libro 136 di 233 - Springer Series in Materials Science
- Brossura
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: 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 experi…ments 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)
Lingua: Inglese
Editore: Springer Verlag, 2017
Serie: Libro 136 di 233 - Springer Series in Materials Science
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Paperback. Condizione: Brand New. reprint edition. 328 pages. 9.25x6.10x0.83 inches. In Stock.
Information Science for Materials Discovery and Design
Lookman, Turab (Edited by)/ Alexander, Francis J. (Edited by)/ Rajan, Krishna (Edited by)
Lingua: Inglese
Editore: Springer, 2015
Serie: Libro 136 di 233 - Springer Series in Materials Science
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Hardcover. Condizione: Brand New. 328 pages. 9.25x6.25x0.75 inches. In Stock.
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Lingua: Inglese
Editore: Springer, 2015
Serie: Libro 136 di 233 - Springer Series in Materials Science
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Gebunden. Condizione: 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-dri…ven approach to plan ma.
Lingua: Inglese
Editore: Springer International Publishing, 2019
Serie: Libro 136 di 233 - Springer Series in Materials Science
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Da: moluna, Greven, Germaniamoluna
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Condizione: 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 approa…ch to plan ma.
Lingua: Inglese
Editore: Springer International Publishing, Springer Nature Switzerland Mär 2019, 2019
Serie: Libro 136 di 233 - Springer Series in Materials Science
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: 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, comb…inatorial 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.
Lingua: Inglese
Editore: Springer International Publishing, Springer Nature Switzerland Dez 2015, 2015
Serie: Libro 136 di 233 - Springer Series in Materials Science
- Rilegato
- Print on Demand
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condizione: 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, 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. 328 pp. Englisch.
Lingua: Inglese
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Serie: Libro 136 di 233 - Springer Series in Materials Science
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Condizione: New. Print on Demand pp. 340.
Lingua: Inglese
Editore: Springer International Publishing, Springer Nature Switzerland Mär 2019, 2019
Serie: Libro 136 di 233 - Springer Series in Materials Science
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 246,09
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Taschenbuch. Condizione: 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, combinat…orial 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.
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing Dez 2015, 2015
Serie: Libro 136 di 233 - Springer Series in Materials Science
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- Print on Demand
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 246,09
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Buch. Condizione: 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 e…xperiments 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.
Lingua: Inglese
Editore: Springer, 2015
Serie: Libro 136 di 233 - Springer Series in Materials Science
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Condizione: New. PRINT ON DEMAND pp. 340.
