Materials Discovery and Design

By Means of Data Science and Optimal Learning

  • Turab Lookman
  • Stephan Eidenbenz
  • Frank Alexander
  • Cris Barnes

Part of the Springer Series in Materials Science book series (SSMATERIALS, volume 280)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Prasanna V. Balachandran, Dezhen Xue, James Theiler, John Hogden, James E. Gubernatis, Turab Lookman
    Pages 59-79
  3. Alisa R. Paterson, Brian J. Reich, Ralph C. Smith, Alyson G. Wilson, Jacob L. Jones
    Pages 81-102
  4. Maxim Ziatdinov, Artem Maksov, Sergei V. Kalinin
    Pages 103-128
  5. Brian M. Patterson, Nikolaus L. Cordes, Kevin Henderson, Xianghui Xiao, Nikhilesh Chawla
    Pages 129-165
  6. Edwin Fohtung, Dmitry Karpov, Tilo Baumbach
    Pages 203-215
  7. Back Matter
    Pages 253-256

About this book


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. 


Data-driven materials science Functionality-driven materials design Combinatorial materials science Large data sets and materials Automated materials design Data Optimization Analysis for Facilities Data-driven materials design

Editors and affiliations

  • Turab Lookman
    • 1
  • Stephan Eidenbenz
    • 2
  • Frank Alexander
    • 3
  • Cris Barnes
    • 4
  1. 1.Theoretical DivisionLos Alamos National LaboratoryLos AlamosUSA
  2. 2.Los Alamos National LaboratoryLos AlamosUSA
  3. 3.Brookhaven National LaboratoryBrookhavenUSA
  4. 4.Los Alamos National LaboratoryLos AlamosUSA

Bibliographic information

Industry Sectors
Energy, Utilities & Environment