Handbook on big data and machine learning in the physical sciences / editors, Surya Kalidindi, GaTech, USA, Sergei V Kalinin, Oak Ridge National Laboratory, USA, Turab Lookman, Los Alamos National Laboratory, USA ; editors-in chief Sergei V Kalinin, Los Alamos National Laboratory, USA, Ian Foster, Argonne National Laboratory, USA & University of Chicago, USA.

Includes bibliographical references and index.

volume 1. Big data methods in experimental materials discovery-- volume 2. Advanced analysis solutions for leading experimental techniques.

"This compendium provides a comprehensive collection of the emergent applications of big data, machine learning, and artificial intelligence technologies to present day physical sciences ranging from materials theory and imaging to predictive synthesis and automated research. This area of research is among the most rapidly developing in the last several years in areas spanning materials science, chemistry, and condensed matter physics. Written by world renowned researchers, the compilation of two authoritative volumes provides a distinct summary of the modern advances in instrument - driven data generation and analytics, establishing the links between the big data and predictive theories, and outlining the emerging field of data and physics-driven predictive and autonomous systems"-- Provided by publisher.