Könyv Informatics for Materials Science and Engineering Krishna Rajan

Informatics for Materials Science and Engineering

Szerző: Krishna Rajan
Nyelv: Angol
Kötés: Kemény kötésű
Elérhetőség: Beszállítói készleten
Küldés 10-18 napon belül
74 066 Ft
Materials informatics: a 'hot topic' area in materials science, aims to combine traditionally bio-le...

Információk a könyvről

Szerző
Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2013
oldal
542
EAN
9780123943996
ISBN
012394399X
Enbook ID
01321917
Súly
880
Méretek
159 x 232 x 36

Teljes leírás

Materials informatics: a 'hot topic' area in materials science, aims to combine traditionally bio-led informatics with computational methodologies, supporting more efficient research by identifying strategies for time- and cost-effective analysis. The discovery and maturation of new materials has been outpaced by the thicket of data created by new combinatorial and high throughput analytical techniques. The elaboration of this "quantitative avalanche" - and the resulting complex, multi-factor analyses required to understand it-means that interest, investment, and research are revisiting informatics approaches as a solution. This work, from Krishna Rajan, the leading expert of the informatics approach to materials, seeks to break down the barriers between data management, quality standards, data mining, exchange, and storage and analysis, as a means of accelerating scientific research in materials science. This solutions-based reference synthesizes foundational physical, statistical, and mathematical content with emerging experimental and real-world applications, for interdisciplinary researchers and those new to the field. It identifies and analyzes interdisciplinary strategies (including combinatorial and high throughput approaches) that accelerate materials development cycle times and reduces associated costs. It offers mathematical and computational analysis that aids formulation of new structure-property correlations among large, heterogeneous, and distributed data sets. It includes practical examples, computational tools, and software analysis that benefits rapid identification of critical data and analysis of theoretical needs for future problems.

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