Materials have the potential to be the centrepiece for the transition to viable renewable energy technologies if they realise a specific suite of properties and achieve a desired set of performance metrics. The envisioned transition involves the discovery of materials that enable generation, conversion, storage, transmission, and utilization of renewable energy. This book presents, through the eye of materials chemistry, an umbrella view of the myriad of classes of materials that make renewable energy technologies work. They are poised to facilitate the transition of non-renewable and unsustainable energy systems of the past into renewable and sustainable energy systems of the future. It is a story that often begins in chemistry laboratories with the discovery of new energy materials. Yet, to displace materials in existing energy technologies with new ones, depends not only on the ability to design and engineer a superior set of performance metrics for the material and the technology but also the requirement to meet a demanding collection of economic, regulatory, social, policy, environmental and sustainability criteria. Disruption in the traditional way of discovering materials is coming with the emergence of artificial intelligence, machine learning and robotic automation designed to accelerate the well-established discovery process, massive libraries of materials can be evaluated and the possibilities are endless. This book provides a perspective on the application of these new technologies to this field as well as an overview of energy materials discovery in the broader techno-economic and social context. Any budding researcher or more experienced materials scientist will find a guide to a fascinating story of discovery and emerge with a vision of what is next.

New technologies are made possible by new materials, and until recently new materials could only be discovered experimentally. Recent advances in solving the crystal structure prediction problem means that the computational design of materials is now a reality. Computational Materials Discovery provides a comprehensive review of this field covering different computational methodologies as well as specific applications of materials design. The book starts by illustrating how and why first-principle calculations have gained importance in the process of materials discovery. The book is then split into three sections, the first exploring different approaches and ideas including crystal structure prediction from evolutionary approaches, data mining methods and applications of machine learning. Section two then looks at examples of designing specific functional materials with special technological relevance for example photovoltaic materials, superconducting materials, topological insulators and thermoelectric materials. The final section considers recent developments in creating low-dimensional materials. With contributions from pioneers and leaders in the field, this unique and timely book provides a convenient entry point for graduate students, researchers and industrial scientists on both the methodologies and applications of the computational design of materials.

The book is concerned with the use of Artificial Intelligence in the discovery, production and application of new engineering materials. Topics covered include nano-robots. data mining, solar energy systems, materials genomics, polymer manufacturing, and energy conversion issues. Keywords: Artificial Intelligence, Mathematical Models, Machine Learning, Artificial Neural Networks, Bayesian Analysis, Vector Machines, Heuristics, Crystal Structure, Component Prediction, Process Optimization, Density Functional Theory, Monitoring, Classification, Nano-Robots, Data Mining, Solar Photovoltaics, Renewable Energy Systems, Alternative Energy Sources, Material Genomics, Polymer Manufacturing, Energy Conversion.

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.

This book comprehensively outlines synchrotron-based X-ray imaging technologies and their associated applications in gaining fundamental insights into the physical and chemical properties as well as reaction mechanisms of energy materials. In this book the major X-ray imaging technologies utilised, depending on research goals and sample specifications, are discussed. With X-ray imaging techniques, the morphology, phase, lattice and strain information of energy materials in both 2D and 3D can be obtained in an intuitive way. In addition, due to the high penetration of X-rays, operando/in situ experiments can be designed to track the qualitative and quantitative changes of the samples during operation. This book will broader the reader’s view on X-ray imaging techniques and inspire new ideas and possibilities in energy materials research.

The book is concerned with the use of Artificial Intelligence in the discovery, production and application of new engineering materials. Topics covered include nano-robots. data mining, solar energy systems, materials genomics, polymer manufacturing, and energy conversion issues. Keywords: Artificial Intelligence, Mathematical Models, Machine Learning, Artificial Neural Networks, Bayesian Analysis, Vector Machines, Heuristics, Crystal Structure, Component Prediction, Process Optimization, Density Functional Theory, Monitoring, Classification, Nano-Robots, Data Mining, Solar Photovoltaics, Renewable Energy Systems, Alternative Energy Sources, Material Genomics, Polymer Manufacturing, Energy Conversion.