书目名称 | Materials Discovery and Design |
副标题 | By Means of Data Sci |
编辑 | Turab Lookman,Stephan Eidenbenz,Cris Barnes |
视频video | |
概述 | Develops a new paradigm for using data science to guide materials discoveries.Describes information-theoretic tools and their application to materials science.Covers both analysis and processing of la |
丛书名称 | Springer Series in Materials Science |
图书封面 |  |
描述 | .This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role of inference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key to achieving the desired goals of real time analysis and feedback. Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and lar |
出版日期 | Book 2018 |
关键词 | Data-driven materials science; Functionality-driven materials design; Combinatorial materials science; |
版次 | 1 |
doi | https://doi.org/10.1007/978-3-319-99465-9 |
isbn_softcover | 978-3-030-07602-3 |
isbn_ebook | 978-3-319-99465-9Series ISSN 0933-033X Series E-ISSN 2196-2812 |
issn_series | 0933-033X |
copyright | Springer Nature Switzerland AG 2018 |