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Information science for materials di...

Lookman, Turab.

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Information science for materials discovery and design

Record Type:	Electronic resources : Monograph/item
Title/Author:	Information science for materials discovery and design/ edited by Turab Lookman, Francis J. Alexander, Krishna Rajan.
other author:	Lookman, Turab.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xvii, 307 p. :ill., digital ;24 cm.
[NT 15003449]:	From 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.
Contained By:	Springer eBooks
Subject:	Materials science. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-23871-5
ISBN:	9783319238715$q(electronic bk.)

Information science for materials discovery and design
Information science for materials discovery and design[electronic resource] /edited by Turab Lookman, Francis J. Alexander, Krishna Rajan. - Cham :Springer International Publishing :2016. - xvii, 307 p. :ill., digital ;24 cm. - Springer series in materials science,v.2250933-033X ;. - Springer series in materials science ;149..

From 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.

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.

ISBN: 9783319238715$q(electronic bk.)

Standard No.: 10.1007/978-3-319-23871-5doiSubjects--Topical Terms:

543314
Materials science.

LC Class. No.: TA403.6

Dewey Class. No.: 620.11

Information science for materials discovery and design
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245 0 0 $a Information science for materials discovery and design $h [electronic resource] / $c edited by Turab Lookman, Francis J. Alexander, Krishna Rajan.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xvii, 307 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in materials science, $x 0933-033X ; $v v.225
505 0 $a From 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.
520 $a 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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650 0 $a Materials science $x Mathematical models. $3 621039
650 0 $a Materials science $x Information resources. $3 2180944
650 1 4 $a Materials Science. $3 890867
650 2 4 $a Nanotechnology. $3 526235
650 2 4 $a Materials Engineering. $3 2071625
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Statistical Physics, Dynamical Systems and Complexity. $3 1066325
650 2 4 $a Characterization and Evaluation of Materials. $3 890988
700 1 $a Lookman, Turab. $3 2180942
700 1 $a Alexander, Francis J. $3 2180943
700 1 $a Rajan, Krishna. $3 2045814
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830 0 $a Springer series in materials science ; $v 149. $3 1565485
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-23871-5
950 $a Chemistry and Materials Science (Springer-11644)