國立高雄大學圖資館 |

Language: English

Back

Information science for materials di...

Alexander, Francis J.

Information science for materials discovery and design

Record Type:	Electronic resources : Monograph/item
Title/Author:	Information science for materials discovery and designedited 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.
Contained By:	Springer eBooks
Subject:	Materials scienceInformation resources.
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 ;v. 62..

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:

738784
Materials science
--Information resources.

LC Class. No.: TA403.6

Dewey Class. No.: 620.11

Information science for materials discovery and design
LDR:03413nmm a2200337 a 4500 001 482210
003 DE-He213
005 20160804092555.0
006 m d
007 cr nn 008maaau
008 161007s2016 gw s 0 eng d
020 $a 9783319238715$q(electronic bk.)
020 $a 9783319238708$q(paper)
024 7 $a 10.1007/978-3-319-23871-5 $2 doi
035 $a 978-3-319-23871-5
040 $a GP $c GP
041 0 $a eng
050 4 $a TA403.6
072 7 $a TBN $2 bicssc
072 7 $a TEC027000 $2 bisacsh
072 7 $a SCI050000 $2 bisacsh
082 0 4 $a 620.11 $2 23
090 $a TA403.6 $b .I43 2016
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.
650 0 $a Materials science $x Information resources. $3 738784
650 1 4 $a Materials Science. $3 273697
650 2 4 $a Nanotechnology. $3 193873
650 2 4 $a Materials Engineering. $3 682881
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Statistical Physics, Dynamical Systems and Complexity. $3 376808
650 2 4 $a Characterization and Evaluation of Materials. $3 273978
650 0 $a Materials science. $3 221779
650 0 $a Materials science $x Mathematical models. $3 266946
700 1 $a Lookman, Turab. $3 738782
700 1 $a Alexander, Francis J. $3 738783
700 1 $a Rajan, Krishna. $3 667476
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Springer series in materials science ; $v v. 62. $3 438346
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-23871-5
950 $a Chemistry and Materials Science (Springer-11644)