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The MRS Symposium Proceeding series is an internationally recognised reference suitable for researchers and practitioners.
Machine learning methods have lowered the cost of exploring new structures of unknown compounds, and can be used to predict reasonable expectations and subsequently validated by experimental results. As new insights and several elaborative tools have been developed for materials science and engineering in recent years, it is an appropriate time to present a book covering recent progress in this field. Searchable and interactive databases can promote research on emerging materials. Recently, databases containing a large number of high-quality materials properties for new advanced materials discovery have been developed. These approaches are set to make a significant impact on human life and, with numerous commercial developments emerging, will become a major academic topic in the coming years. This authoritative and comprehensive book will be of interest to both existing researchers in this field as well as others in the materials science community who wish to take advantage of these powerful techniques. The book offers a global spread of authors, from USA, Canada, UK, Japan, France, Russia, China and Singapore, who are all world recognized experts in their separate areas. With content relevant to both academic and commercial points of view, and offering an accessible overview of recent progress and potential future directions, the book will interest graduate students, postgraduate researchers, and consultants and industrial engineers.
Combinatorial Materials Science describes new developments and research results in catalysts, biomaterials, and nanomaterials, together with informatics approaches to the analysis of Combinatorial Science (CombiSci) data. CombiSci has been used extensively in the pharmaceutical industry, but there is enormous potential in its application to materials design and characterization. Addressing advances and applications in both fields, Combinatorial Materials Science: Integrates the scientific fundamentals and interdisciplinary underpinnings required to develop and apply CombiSci concepts Discusses the development and use of CombiSci for the systematic and accelerated investigation of new phenomena and of the complex structure-function interplay in materials Covers the development of new library design strategies for materials processing and for high-throughput tools for rapid sampling Uses a unique, unified approach of applying combinatorial methods to unravel the non-linear structure-function relationships in diverse materials (both hard and soft), together with advances in informatics With chapters written by leading researchers in their specialty areas, this authoritative guide is a must-have resource for scientists and engineers in materials science research, biochemists, chemists, immunologists, cell biologists, polymer scientists, chemical and mechanical engineers, statisticians, and computer scientists. It is also a great text for graduate-level courses in materials science/engineering, polymer science, chemical engineering, and chemistry.
The Reviews in Computational Chemistry series brings together leading authorities in the field to teach the newcomer and update the expert on topics centered on molecular modeling, such as computer-assisted molecular design (CAMD), quantum chemistry, molecular mechanics and dynamics, and quantitative structure-activity relationships (QSAR). This volume, like those prior to it, features chapters by experts in various fields of computational chemistry. Topics in Volume 29 include: Noncovalent Interactions in Density-Functional Theory Long-Range Inter-Particle Interactions: Insights from Molecular Quantum Electrodynamics (QED) Theory Efficient Transition-State Modeling using Molecular Mechanics Force Fields for the Everyday Chemist Machine Learning in Materials Science: Recent Progress and Emerging Applications Discovering New Materials via a priori Crystal Structure Prediction Introduction to Maximally Localized Wannier Functions Methods for a Rapid and Automated Description of Proteins: Protein Structure, Protein Similarity, and Protein Folding
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.
Chemical sensors are in high demand for applications as varied as water pollution detection, medical diagnostics, and battlefield air analysis. Designing the next generation of sensors requires an interdisciplinary approach. The book provides a critical analysis of new opportunities in sensor materials research that have been opened up with the use of combinatorial and high-throughput technologies, with emphasis on experimental techniques. For a view of component selection with a more computational perspective, readers may refer to the complementary volume of Integrated Analytical Systems edited by M. Ryan et al., entitled “Computational Methods for Sensor Material Selection”.
Materials informatics: a 'hot topic' area in materials science, aims to combine traditionally bio-led informatics with computational methodologies, supporting more efficient research by identifying strategies for time- and cost-effective analysis. The discovery and maturation of new materials has been outpaced by the thicket of data created by new combinatorial and high throughput analytical techniques. The elaboration of this "quantitative avalanche"—and the resulting complex, multi-factor analyses required to understand it—means that interest, investment, and research are revisiting informatics approaches as a solution. This work, from Krishna Rajan, the leading expert of the informatics approach to materials, seeks to break down the barriers between data management, quality standards, data mining, exchange, and storage and analysis, as a means of accelerating scientific research in materials science. This solutions-based reference synthesizes foundational physical, statistical, and mathematical content with emerging experimental and real-world applications, for interdisciplinary researchers and those new to the field. - Identifies and analyzes interdisciplinary strategies (including combinatorial and high throughput approaches) that accelerate materials development cycle times and reduces associated costs - Mathematical and computational analysis aids formulation of new structure-property correlations among large, heterogeneous, and distributed data sets - Practical examples, computational tools, and software analysis benefits rapid identification of critical data and analysis of theoretical needs for future problems
The development of parallel synthesis and high-throughput characterization tools offer scientists a time-efficient and cost-effective solution for accelerating traditional synthesis processes and developing the structure-property relationships of multiple materials under variable conditions. Written by renowned contributors to the field, Combina
Quality measurement, control, and improvement in combinatorial chemistry Combinatorial chemistry has developed rapidly in the past decade, with great advances made by scientists working on analysis and purification of a large number of compounds and the analysis of polymer-bound compounds. However, formidable challenges lie ahead of today's researcher. For example, high-throughput analysis and purification technologies must be further developed to ensure combinatorial libraries are "purifiable," and "drugable." To this end, Analysis and Purification Methods in Combinatorial Chemistry describes various analytical techniques and systems for the development, validation, quality control, purification, and physicochemical testing of combinatorial libraries. A new volume in Wiley's Chemical Analysis series, this text has four parts covering: * Various approaches to monitoring reactions on solid support and optimizing reactions for library synthesis * High-throughput analytical methods used to analyze the quality of libraries * High-throughput purification techniques * Analytical methods applied in post-synthesis and post-purification stages Drawing from the contributions of respected experts in combinatorial chemistry, this comprehensive book provides coverage of applications of Nuclear Magnetic Resonance (NMR), liquid chromatography/mass spectrometry (LC/MS), Fourier Transform Infrared (FTIR), micellar electrokinetic chromatography (MEKC) technologies, as well as other analytical techniques. This eminently useful volume is an essential addition to the library of students and researchers studying or working in analytical chemistry, combinatorial chemistry, medicinal chemistry, organic chemistry, biotechnology, biochemistry, or biophysics.