000			03148 a2200301 4500
003			OSt
005			20230110172504.0
008			230110b xxu||||| |||| 00| 0 eng d
020			_a9783319994642
040			_cIIT Kanpur
041			_aeng
082			_a620.11 _bM418
245			_aMaterials discovery and design _bby means of data science and optimal learning _cedited by Turab Lookman ...[et al.]
260			_bSpringer _c2018 _aSwitzerland
300			_axvi, 256p
440			_aSpringer series in materials science
490			_a/ edited by Robert Hull ; v.280
520			_aThis 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 large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes of in situ spatially and temporally resolved data per sample. The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader.
650			_aData mining
650			_aMachine learning
650			_aMaterials science -- Data processing
650			_aMaterials science
700			_aLookman, Turab [ed.]
700			_aEidenbenz, Stephan [ed.]
700			_aAlexander, Frank [ed.]
700			_aBarnes, Cris [ed.]
942			_cBK
999			_c566302 _d566302