Informatics for Materials Science and Engineering

Krishna Rajan is the SUNY Distinguished Professor and Erich Bloch Chair of the Department of Materials Design and Innovation (MDI) at the University at Buffalo; with a joint appointment as Chief Scientist in the Energy Processes and Materials Division at Pacific Northwest National Laboratory (PNNL). He has pioneered the field of Materials Informatics and data driven discovery in materials science and engineering and its impact on characterization, processing, and modeling of materials. He has received numerous recognitions including the Alexander von Humboldt Award from Germany, the CSIRO- Australia Distinguished Visiting Scientist Award, the CNRS Visiting Professorship from France and the Presidential Lecture Award from the National Institute of Materials Science, Japan.Dr Rajan received his undergraduate degree in Metallurgy and Materials Science from the University of Toronto followed by a doctorate in Materials Science from MIT with a minor in Science and Technology policy. He subsequently held post-doctoral appointments at MIT and Cambridge University. He was a staff scientist at the National Research Council of Canada, followed by faculty positions at Rensselaer Polytechnic Institute and Iowa State University before coming to the University at Buffalo as the founding chair of the MDI department. It is the first department that has its research and curriculum built around an informatics perspective of materials science and engineering.

"The first half of the volume sets out foundational aspects of data science, and the second half surveys applications in materials science using a case-study approach. The topics include novel approaches to statistical learning in materials science, data dimensionality reduction in materials science,…. high-performance computing for accelerated zeolitic materials modeling, and using multivariate analysis to answer questions concerning the conservation of artworks and cultural heritage materials." --Reference & Research Book News, December 2013

• Preface: A Reading Guide xiiiAcknowledgment xv1. Materials Informatics: An Introduction 12. Data Mining in Materials Science and Engineering 173. Novel Approaches to Statistical Learning in Materials Science 374. Cluster Analysis: Finding Groups in Data 535. Evolutionary Data-Driven Modeling 716. Data Dimensionality Reduction in Materials Science 977. Visualization in Materials Research: Rendering Strategiesof Large Data Sets 1218. Ontologies and Databases < Knowledge Engineeringfor Materials Informatics 1479. Experimental Design for Combinatorial Experiments 18910. Materials Selection for Engineering Design 21911. Thermodynamic Databases and Phase Diagrams 24512. Towards Rational Design of Sensing Materialsfrom Combinatorial Experiments 27113. High-Performance Computing for Accelerated ZeoliticMaterials Modeling 31514. Evolutionary Algorithms Applied to Electronic-StructureInformatics: Accelerated Materials Design Using DataDiscovery vs. Data Searching 34915. Informatics for Crystallography: Designing Structure Maps 36516. From Drug Discovery QSAR to Predictive Materials QSPR:The Evolution of Descriptors, Methods, and Models 38517. Organic Photovoltaics 42318. Microstructure Informatics 44319. Artworks and Cultural Heritage Materials: Using MultivariateAnalysis to Answer Conservation Questions 46720. Data Intensive Imaging and Microscopy: A MultidimensionalData Challenge 495References 510Index 513