Information Science for Materials Discovery and Design

Chapter 1 A Perspective on Materials Informatics: State-of-the-Art and Challenges

Chapter 2 Information-Driven Experimental Design in Materials Science

Chapter 3 Bayesian Optimization for Materials Design

Chapter 4 Small-Sample Classification

Chapter 6 Inference of Hidden Structures in Complex Physical Systems by Multi-scale Clustering

Chapter 7 On the Use of Data Mining Techniques to Build High-Density, Additively-Manufactured Parts

Chapter 8 Optimal Dopant Selection for Water Splitting with Cerium Oxides: Mining and Screening First Principles Data

Chapter 9 Toward Materials Discovery with First-Principles Datasets and Learning Methods

Chapter 10 Materials Informatics Using Ab initio Data: Application to MAX Phases

Chapter 11 Symmetry-Adapted Distortion Modes as Descriptors for Materials Informatics

Chapter 12 Discovering Electronic Signatures for Phase Stability of Intermetallics via Machine Learning

Chapter 13 Combinatorial Materials Science, and a Perspective on Challenges in Data Acquisition, Analysis and Presentation

Chapter 14 High Throughput Combinatorial Experimentation + Informatics = Combinatorial Science