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Titlebook: Information Science for Materials Discovery and Design; Turab Lookman,Francis J. Alexander,Krishna Rajan Book 2016 Springer International

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書目名稱Information Science for Materials Discovery and Design
編輯Turab Lookman,Francis J. Alexander,Krishna Rajan
視頻videohttp://file.papertrans.cn/466/465239/465239.mp4
概述One of the first books on materials discovery strategy.Emphasizes the paradigm of codesign.Brings together diverse expertise to improve the model for materials discovery.Includes supplementary materia
叢書名稱Springer Series in Materials Science
圖書封面Titlebook: Information Science for Materials Discovery and Design;  Turab Lookman,Francis J. Alexander,Krishna Rajan Book 2016 Springer International
描述.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 mate
出版日期Book 2016
關(guān)鍵詞Accelerated Materials Discovery; Applying MQSPRs; Code Sign; Complex Formulations and Molecules; Data-dr
版次1
doihttps://doi.org/10.1007/978-3-319-23871-5
isbn_softcover978-3-319-79541-6
isbn_ebook978-3-319-23871-5Series ISSN 0933-033X Series E-ISSN 2196-2812
issn_series 0933-033X
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

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Small-Sample Classificationions can be used to obtain bounds on error estimation accuracy. Given the necessity of modeling assumptions, we go on to discuss minimum-mean-square-error (MMSE) error estimation and the design of optimal classifiersrelative to prior knowledge and data in a Bayesian context.
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