Download or read book Combinatorial and artificial intelligence methods in materials science written by and published by . This book was released on 2003 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Symposium written by Ichirō Takeuchi and published by . This book was released on 2002 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Combinatorial and Artificial Intelligence Methods in Materials Science II Volume 804 written by Radislav A. Potyrailo and published by . This book was released on 2004-03-22 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: The MRS Symposium Proceeding series is an internationally recognised reference suitable for researchers and practitioners.
Download or read book Combinatorial and Artificial Intelligence Methods in Materials Science II written by Radislav A. Potyrailo and published by . This book was released on 2004 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Combinatorial and Artificial Intelligence Methods in Materials Science II Proceedings of Symposium JJ 1 4 December 2003 Boston Massachusetts written by and published by . This book was released on 2004 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Combinatorial and Artificial Intelligence Methods in Materials Science written by Ichiro Takeuchi and published by . This book was released on 2002-01-01 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Combinatorial and Artificial Intelligence Methods in Materials Science written by and published by . This book was released on 2003 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Materials Science and Engineering written by James N. Cawse and published by Elsevier Inc. Chapters. This book was released on 2013-07-10 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: Design of combinatorial and high-throughput experiments has continued to build on the progress of the last two decades. New variations of factorial and mixture designs have expanded their capability. Increasing attention is being paid to adapting designs to the constraints of the physical apparatus, in the form of split-plot methods or conscious understanding of the statistical penalties to be paid. Rapidly increasing computer power has allowed the use of more sophisticated algorithmic designs and evolutionary methods. Finally, descriptor-based design and analysis of data is making steady progress and there are hopes of its reaching a mature state in the coming decade.
Download or read book Combinatorial Materials Science written by Marc D. Porter and published by John Wiley & Sons. This book was released on 2007-08-17 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Combinatorial Methods and Informatics in Materials Science Volume 894 written by M. J. Fasolka and published by . This book was released on 2006-05-17 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combinatorial and high-throughput experimental approaches and related informatics, modeling and data-mining methods have permitted researchers to accelerate the pace at which new, complex materials and device systems are discovered, optimized and understood. Today, the development and application of these revolutionary approaches continue to grow and diversify. This book offers an international, interdisciplinary perspective for scientists and engineers interested in combinatorial, high-throughput and advanced informatics approaches to materials research. The range of disciplines includes materials science; chemistry; physics; electrical, chemical and mechanical engineering; materials modeling; and data systems engineering. Presentations share successful studies, and illuminate current and emerging challenges in areas including: the design and fabrication of combinatorial libraries for materials and devices; high-throughput characterization methods for such systems; automation of instrumentation and data analysis; advanced modeling and data mining techniques for rapid materials design and properties prediction; and data system design and software for combinatorial workflows.
Download or read book Artificial Intelligence for Materials Science written by Yuan Cheng and published by Springer Nature. This book was released on 2021-03-26 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Materials Discovery and Design written by Turab Lookman and published by Springer. This book was released on 2018-09-22 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Hands On Mathematics for Deep Learning written by Jay Dawani and published by Packt Publishing Ltd. This book was released on 2020-06-12 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.
Download or read book Computational Methods for Sensor Material Selection written by Margaret A. Ryan and published by Springer Science & Business Media. This book was released on 2009-10-06 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chemical vapor sensing arrays have grown in popularity over the past two decades, finding applications for tasks such as process control, environmental monitoring, and medical diagnosis. This is the first in-depth analysis of the process of choosing materials and components for these "electronic noses", with special emphasis on computational methods. For a view of component selection with an experimental perspective, readers may refer to the complementary volume of Integrated Microanalytical Systems entitled "Combinatorial Methodologies for Sensor Materials."
Download or read book Combinatorial Methods for Chemical and Biological Sensors written by Radislav A. Potyrailo and published by Springer Science & Business Media. This book was released on 2009-03-21 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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”.
Download or read book High Throughput Analysis written by Radislav A. Potyrailo and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 642 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, edited by Potyrailo and Amis, addresses a new paradigm-shifting approach in the search for new materials-Combinatorial Materials Science. One way to consider such an approach is to imagine an adventurous chef who decides to look for new entrees by cooking food ingredients in many pots using different combinations in every pot, and boil ing, steaming, or frying them in various ways. Although most of the pots will not have the tastiest food ever devised, some recipes will taste intriguing, and some eventually will lead to a discovery of a new fascinating cuisine. Of course, having a skilled chef design the com binatorial formulation will certainly be helpful in ensuring a successful outcome. Similar to food, each engineering material is a complex product of its chemical composition, structure, and processing. Generally, each of these components matters---change one and you get another material. Most of these "new" materials will be less good than ones we use now since existing materials have been refined with the extensive work of scientists and engi neers. At the same time if one prepares diverse materials like our adventurous chef, chang ing material composition, processing conditions and time, etc. , some of these materials will be superior to existing ones and a few might represent breakout technology.
Download or read book Reviews in Computational Chemistry Volume 29 written by Abby L. Parrill and published by John Wiley & Sons. This book was released on 2016-04-11 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
