© 2016

Information Science for Materials Discovery and Design

  • Turab Lookman
  • Francis J. Alexander
  • Krishna Rajan


  • One of the first books on materials discovery strategy

  • Emphasizes the paradigm of codesign

  • Brings together diverse expertise to improve the model for materials discovery


Part of the Springer Series in Materials Science book series (SSMATERIALS, volume 225)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Data Analytics and Optimal Learning

    1. Front Matter
      Pages 1-1
    2. T. Lookman, P. V. Balachandran, D. Xue, G. Pilania, T. Shearman, J. Theiler et al.
      Pages 3-12
    3. R. Aggarwal, M. J. Demkowicz, Y. M. Marzouk
      Pages 13-44
    4. Peter I. Frazier, Jialei Wang
      Pages 45-75
    5. Lori A. Dalton, Edward R. Dougherty
      Pages 77-101
    6. J. E. Gubernatis
      Pages 103-113
    7. Z. Nussinov, P. Ronhovde, Dandan Hu, S. Chakrabarty, Bo Sun, Nicholas A. Mauro et al.
      Pages 115-138
  3. Materials Prediction with Data, Simulations and High-throughput Calculations

  4. Combinatorial Materials Science with High-throughput Measurements and Analysis

  5. Back Matter
    Pages 301-307

About this book


This book deals with an information-driven approach to plan materials discovery and design, iterative learning. The authors present contrasting but complementary approaches, such as those based on high throughput calculations, combinatorial experiments or data driven discovery, together with machine-learning methods. Similarly, statistical methods successfully applied in other fields, such as biosciences, are presented. The content spans from materials science to information science to reflect the cross-disciplinary nature of the field. A perspective is presented that offers a paradigm (codesign loop for materials design) to involve iteratively learning from experiments and calculations to develop materials with optimum properties. Such a loop requires the elements of incorporating domain materials knowledge, a database of descriptors (the genes), a surrogate or statistical model developed to predict a given property with uncertainties, performing adaptive experimental design to guide the next experiment or calculation and aspects of high throughput calculations as well as experiments. The book is about manufacturing with the aim to halving the time to discover and design new materials. Accelerating discovery relies on using large databases, computation, and mathematics in the material sciences in a manner similar to the way used to in the Human Genome Initiative. Novel approaches are therefore called to explore the enormous phase space presented by complex materials and processes. To achieve the desired performance gains, a predictive capability is needed to guide experiments and computations in the most fruitful directions by reducing not successful trials. Despite advances in computation and experimental techniques, generating vast arrays of data; without a clear way of linkage to models, the full value of data driven discovery cannot be realized. Hence, along with experimental, theoretical and computational materials science, we need to add a “fourth leg’’ to our toolkit to make the “Materials Genome'' a reality, the science of Materials Informatics.


Accelerated Materials Discovery Applying MQSPRs Code Sign Complex Formulations and Molecules Data-driven Discovery of Materials Datamining in Materials Science Informatics for Property-processing Linkage Materials Genome Materials Informatics Minimal Peptide Substrates Model-based Classification Properties of MAX Phase Compounds

Editors and affiliations

  • Turab Lookman
    • 1
  • Francis J. Alexander
    • 2
  • Krishna Rajan
    • 3
  1. 1.Theoretical DivisionLos Alamos National LaboratoryLos AlamosUSA
  2. 2.Computer and Communications ServiceLos Alamos National Laboratory Computer and Communications ServiceLOS ALAMOSUSA
  3. 3.Department of Materials Design and InnovationUniversity at Buffalo- The State University of New YorkBuffaloUSA

Bibliographic information

Industry Sectors
Oil, Gas & Geosciences