The development of new materials for manufacturing and in processing (such as new catalysts) has undergone a major transformation from the Edisonian hit-or-miss try-many-guesses approach that hopes to stumble onto the serendipitous discovery of a substantially improved material. Even though there is substantial documentation of applying classical factor-factorial statistical approaches in material formulations, the body of work ignores the problem of discovering which properties of a class of materials are important and how then to use or manipulate these properties to achieve the improvements desired.

The approaches described in this book combine Bayesian statistical analysis, big data informatics analysis including visualization of correlations among properties, machine learning to uncover hidden properties, and combinatorics. Theoretical calculations of electronic properties of materials (often quantum mechanical using density functional theory) play an important role.

This book presents reports given in a workshop sponsored by Los Alamos National Laboratory in 2014. The methods described originated in the human genome project and have been used in organic chemistry for prescreening candidate molecules in drug development. The application of these techniques in materials development is a much more challenging problem because these materials are usually solids in different crystal habits, consist of several elements often in nonstoichiometric proportions, and sometimes are limited to surface behavior or thin films on a solid substrate. This volume documents the progress made in applying these techniques.

There are 14 chapters divided into three parts. The first part, with six chapters, is called “Data Analytics and Optimal Learning.” After an introductory chapter on the state of the art and challenges, the remaining chapters in this part describe the theoretical basis and applied mathematics of the approaches used. Experimental design based on Bayesian inference is presented in chapters 2 and 3. The Bayesian approach is used to infer both parameters and appropriate models at the molecular/solid state phase level of material composition. Chapter 4 is devoted to small sample analysis since often it is impractical to prepare and test many different variations of the materials. Chapters 5 & 6 employ data visualization and multiscale modeling techniques to reveal correlated properties, coupled structural parameters, and hidden properties. Although the emphasis in this part is on theory, the contributors include substantial experimental evidence to illustrate their arguments.

Parts 2 and 3 emphasize more directly the computational supports applied to examples taken from the laboratory. The two parts differ in the type of computations. Part 2, on high-throughput calculations, also has six chapters. Chapter 7 applies data mining techniques to improving the manufacture of parts by laser fusion of powders. Chapter 8 shows how the techniques are used in selecting the best dopants in cerium oxide catalysts used to split water into hydrogen and oxygen gases. Chapters 9 and 10 reverse the approach by creating databases using top-down first principles by computing the properties of materials using density functional theory calculations, which are then used in conjunction with experiments. Chapters 11 and 12 focus on solid state structures. In the first of the two, properties of materials with perovskite structures are correlated against the distortion modes of the solid state structures. In the second, machine learning is used to discover electronic signatures associated with phase stability of solids.

The computational emphasis in Part 3 is combinatorics. Only two chapters are in this part. Combinatorics applied to solid state materials is much more challenging than in organic chemistry because of the possibility of large variations and continual variations in composition and the changes in properties that these variations can cause. In order to manage the computational complexity associated with combinatorics, the complexity of the materials must be reduced by clustering composition-property data and using simplex and statistical methods to achieve simplification.

This book impressed me personally because four decades ago I worked for a company developing catalysts for processing petroleum using Edisonian techniques with only the slightest amount of experimental physical and chemical data and virtually no theoretical computations for guidance. There was nothing upon which to base a systematic protocol for improving the materials and processes. This book takes the theory and practice of experimental design of new materials to a new level.