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This book provides a comprehensive introduction to ferroics and frustrated materials. Ferroics comprise a range of materials classes with functionalities such as magnetism, polarization, and orbital degrees of freedom and strain. Frustration, due to geometrical constraints, and disorder, due to chemical and/or structural inhomogeneities, can lead to glassy behavior, which has either been directly observed or inferred in a range of materials classes from model systems such as artificial spin ice, shape memory alloys, and ferroelectrics to electronically functional materials such as manganites. Interesting and unusual properties are found to be associated with these glasses and have potential for novel applications. Just as in prototypical spin glass and structural glasses, the elements of frustration and disorder lead to non-ergodocity, history dependence, frequency dependent relaxation behavior, and the presence of inhomogeneous nano clusters or domains. In addition, there are new states of matter, such as spin ice; however, it is still an open question as to whether these systems belong to the same family or universality class. The purpose of this work is to collect in a single volume the range of materials systems with differing functionalities that show many of the common characteristics of geometrical frustration, where interacting degrees of freedom do not fit in a lattice or medium, and glassy behavior is accompanied by additional presence of disorder. The chapters are written by experts in their fields and span experiment and theory, as well as simulations. Frustrated Materials and Ferroic Glasses will be of interest to a wide range of readers in condensed matter physics and materials science. Brings together experts in glasses, geometrical frustration, and functional materials Covers theory, experiment, and simulations of ferroics Features an easy-to-read introduction in each chapter to make specialized topics accessible to a broad readership in condensed matter physics and materials science.

Materials science. --- Material science --- Physical sciences --- Magnetism. --- Engineering. --- Nanochemistry. --- Optical materials. --- Magnetism, Magnetic Materials. --- Ceramics, Glass, Composites, Natural Materials. --- Nanotechnology and Microengineering. --- Optical and Electronic Materials. --- Solid State Physics. --- Optics --- Materials --- Nanoscale chemistry --- Chemistry, Analytic --- Nanoscience --- Construction --- Industrial arts --- Technology --- Mathematical physics --- Physics --- Electricity --- Magnetics --- Analytical chemistry --- Magnetic materials. --- Ceramics. --- Glass. --- Composites (Materials). --- Composite materials. --- Nanotechnology. --- Electronic materials. --- Solid state physics. --- Solids --- Electronic materials --- Molecular technology --- Nanoscale technology --- High technology --- Composites (Materials) --- Multiphase materials --- Reinforced solids --- Solids, Reinforced --- Two phase materials --- Amorphous substances --- Ceramics --- Glazing --- Ceramic technology --- Industrial ceramics --- Keramics --- Building materials --- Chemistry, Technical --- Clay

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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.

Materials Science --- Technology - General --- Chemical & Materials Engineering --- Engineering & Applied Sciences --- Materials science. --- Materials science --- Mathematical models. --- Material science --- Physical sciences --- Nanotechnology. --- Data mining. --- Surfaces (Physics). --- Statistical physics. --- Materials Engineering. --- Data Mining and Knowledge Discovery. --- Complex Systems. --- Characterization and Evaluation of Materials. --- Statistical Physics and Dynamical Systems. --- Physics --- Mathematical statistics --- Surface chemistry --- Surfaces (Technology) --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Molecular technology --- Nanoscale technology --- High technology --- Statistical methods --- Engineering—Materials. --- Dynamical systems. --- Dynamical systems --- Kinetics --- Mathematics --- Mechanics, Analytic --- Force and energy --- Mechanics --- Statics

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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. .

Data mining. --- Materials science. --- Material science --- Physical sciences --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Surfaces (Physics). --- Computer science. --- Numerical analysis. --- Numerical and Computational Physics, Simulation. --- Characterization and Evaluation of Materials. --- Data Mining and Knowledge Discovery. --- Materials Engineering. --- Computational Science and Engineering. --- Numerical Analysis. --- Mathematical analysis --- Informatics --- Science --- Physics --- Surface chemistry --- Surfaces (Technology) --- Physics. --- Engineering—Materials. --- Computer mathematics. --- Computer mathematics --- Electronic data processing --- Mathematics --- Natural philosophy --- Philosophy, Natural --- Dynamics

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• Reference Manager
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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. .

Numerical analysis --- Physics --- Surface chemistry --- Materials sciences --- Computer science --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- materiaalkennis --- oppervlakte-onderzoek --- datamining --- big data --- data science --- computers --- informatica --- simulaties --- informaticaonderzoek --- fysica --- computerkunde --- data acquisition --- numerieke analyse