Materials Discovery and Design: By Means of Data Science and Optimal Learning (Springer Series in Materials Science Book 280)

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 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. Front Matter ....Pages i-xvi Dimensions, Bits, and Wows in Accelerating Materials Discovery (Lav R. Varshney)....Pages 1-14 Is Automated Materials Design and Discovery Possible? (Michael McKerns)....Pages 15-58 Importance of Feature Selection in Machine Learning and Adaptive Design for Materials (Prasanna V. Balachandran, Dezhen Xue, James Theiler, John Hogden, James E. Gubernatis, Turab Lookman)....Pages 59-79 Bayesian Approaches to Uncertainty Quantification and Structure Refinement from X-Ray Diffraction (Alisa R. Paterson, Brian J. Reich, Ralph C. Smith, Alyson G. Wilson, Jacob L. Jones)....Pages 81-102 Deep Data Analytics in Structural and Functional Imaging of Nanoscale Materials (Maxim Ziatdinov, Artem Maksov, Sergei V. Kalinin)....Pages 103-128 Data Challenges of In Situ X-Ray Tomography for Materials Discovery and Characterization (Brian M. Patterson, Nikolaus L. Cordes, Kevin Henderson, Xianghui Xiao, Nikhilesh Chawla)....Pages 129-165 Overview of High-Energy X-Ray Diffraction Microscopy (HEDM) for Mesoscale Material Characterization in Three-Dimensions (Reeju Pokharel)....Pages 167-201 Bragg Coherent Diffraction Imaging Techniques at 3rd and 4th Generation Light Sources (Edwin Fohtung, Dmitry Karpov, Tilo Baumbach)....Pages 203-215 Automatic Tuning and Control for Advanced Light Sources (Alexander Scheinker)....Pages 217-251 Back Matter ....Pages 253-256