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Data Based Methods For Materials Design And Discovery
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Book Synopsis Data-Based Methods for Materials Design and Discovery by : Ghanshyam Pilania
Download or read book Data-Based Methods for Materials Design and Discovery written by Ghanshyam Pilania and published by Springer Nature. This book was released on 2022-05-31 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning methods are changing the way we design and discover new materials. This book provides an overview of approaches successfully used in addressing materials problems (alloys, ferroelectrics, dielectrics) with a focus on probabilistic methods, such as Gaussian processes, to accurately estimate density functions. The authors, who have extensive experience in this interdisciplinary field, discuss generalizations where more than one competing material property is involved or data with differing degrees of precision/costs or fidelity/expense needs to be considered.
Book Synopsis Information Science for Materials Discovery and Design by : Turab Lookman
Download or read book Information Science for Materials Discovery and Design written by Turab Lookman and published by Springer. This book was released on 2015-12-12 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Materials Discovery and Design by : Turab Lookman
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 266 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.
Book Synopsis Computational Materials Discovery by : Artem Oganov
Download or read book Computational Materials Discovery written by Artem Oganov and published by Royal Society of Chemistry. This book was released on 2018-10-30 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unique and timely book providing an overview of both the methodologies and applications of computational materials design.
Book Synopsis Artificial Intelligence for Materials Science by : Yuan Cheng
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.
Book Synopsis Data-Driven Science and Engineering by : Steven L. Brunton
Download or read book Data-Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Book Synopsis Computational Approaches to Materials Design by : Shubhabrata Datta
Download or read book Computational Approaches to Materials Design written by Shubhabrata Datta and published by Engineering Science Reference. This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brings together empirical research, theoretical concepts, and the various approaches in the design and discovery of new materials. Thois volume highlights optimization tools and soft computing methods, and is ideal for researchers, both in academia and in industrial settings, and practitioners who are interested in the application of computational techniques in materials engineering.
Book Synopsis Computational Materials Design by : Tetsuya Saito
Download or read book Computational Materials Design written by Tetsuya Saito and published by Nelson Thornes. This book was released on 1999-07-23 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book consists of ten chapters which outline a wide range of technologies from first-principle calculations to continuum mechanics, with applications to materials design and development. Written with a clear exposition, this book will be invaluable for engineers who want to learn about the modern technologies and techniques utilized in materials design.
Book Synopsis Materials Informatics by : Olexandr Isayev
Download or read book Materials Informatics written by Olexandr Isayev and published by John Wiley & Sons. This book was released on 2019-12-04 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides everything readers need to know for applying the power of informatics to materials science There is a tremendous interest in materials informatics and application of data mining to materials science. This book is a one-stop guide to the latest advances in these emerging fields. Bridging the gap between materials science and informatics, it introduces readers to up-to-date data mining and machine learning methods. It also provides an overview of state-of-the-art software and tools. Case studies illustrate the power of materials informatics in guiding the experimental discovery of new materials. Materials Informatics: Methods, Tools and Applications is presented in two parts?Methodological Aspects of Materials Informatics and Practical Aspects and Applications. The first part focuses on developments in software, databases, and high-throughput computational activities. Chapter topics include open quantum materials databases; the ICSD database; open crystallography databases; and more. The second addresses the latest developments in data mining and machine learning for materials science. Its chapters cover genetic algorithms and crystal structure prediction; MQSPR modeling in materials informatics; prediction of materials properties; amongst others. -Bridges the gap between materials science and informatics -Covers all the known methodologies and applications of materials informatics -Presents case studies that illustrate the power of materials informatics in guiding the experimental quest for new materials -Examines the state-of-the-art software and tools being used today Materials Informatics: Methods, Tools and Applications is a must-have resource for materials scientists, chemists, and engineers interested in the methods of materials informatics.
Book Synopsis Machine Learning by : Kevin P. Murphy
Download or read book Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2012-08-24 with total page 1102 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Book Synopsis Informatics for Materials Science and Engineering: Data-Driven Discovery for Accelerated Experimentation and Application by : Krishna Rajan
Download or read book Informatics for Materials Science and Engineering: Data-Driven Discovery for Accelerated Experimentation and Application written by Krishna Rajan and published by Butterworth-Heinemann. This book was released on 2017-11-13 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 "
Book Synopsis The Fourth Paradigm by : Anthony J. G. Hey
Download or read book The Fourth Paradigm written by Anthony J. G. Hey and published by . This book was released on 2009 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foreword. A transformed scientific method. Earth and environment. Health and wellbeing. Scientific infrastructure. Scholarly communication.
Book Synopsis Artificial Intelligence in Drug Discovery by : Nathan Brown
Download or read book Artificial Intelligence in Drug Discovery written by Nathan Brown and published by Royal Society of Chemistry. This book was released on 2020-11-04 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.
Book Synopsis Knowledge Guided Machine Learning by : Anuj Karpatne
Download or read book Knowledge Guided Machine Learning written by Anuj Karpatne and published by CRC Press. This book was released on 2022-08-15 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML
Book Synopsis Interatomic Forces in Condensed Matter by : Mike Finnis
Download or read book Interatomic Forces in Condensed Matter written by Mike Finnis and published by Oxford Series on Materials Mod. This book was released on 2003 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is a continuing growth of interest in the computer simulation of materials at the atomic scale, using a variety of academic and commercial computer programs. In all such programs there is some physical model of the inter-atomic forces, which may be based on something as simple as a pair interaction, such as the Lennard-Jones model, or as complex as a self-consistent, all-electron solution of the quantum mechanical problem. For a student or researcher, the basis of such models is often shrouded in mystery. It is usually unclear how well founded they are, since it is hard to find a discussion of the physical assumptions that have been made in their construction. The lack of clear understanding of the scope and limitations of a given model may lead to its innocent misuse, resulting either in unfair criticism of the model or in the dissemination of nonsensical results. In the present book, models of inter-atomic forces are derived from a common physical basis, namely the density functional theory. The interested reader will be able to follow the detailed derivation of pairwise potentials in simple metals, tight-binding models from the simplest to the most sophisticated (self-consistent) kind, and various ionic models. The book is self-contained, requiring no more background than provided by an undergraduate quantum mechanics course. It aims to furnish the reader with a critical appreciation of the broad range of models in current use, and to provide the tools for understanding other variants that are described in the literature. Some of the material is new, and some pointers are given to possible future avenues of model development.
Book Synopsis Multifunctional Metasurfaces by : He-Xiu Xu
Download or read book Multifunctional Metasurfaces written by He-Xiu Xu and published by Springer Nature. This book was released on 2022-06-01 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, we have witnessed a rapid expansion of using super-thin metasurfaces to manipulate light or electromagnetic wave in a subwavelength scale. However, most designs are confined to a passive scheme and monofunctional operation, which hinders considerably the promising applications of the metasurfaces. Specifically, the tunable and multifunctional metasurfaces enable to facilitate switchable functionalities and multiple functionalities which are extremely essential and useful for integrated optics and microwaves, well alleviating aforementioned issues. In this book, we introduce our efforts in exploring the physics principles, design approaches, and numerical and experimental demonstrations on the fascinating functionalities realized. We start by introducing in Chapter 2 the "merging" scheme in constructing multi-functional metadevices, paying particular attention to its shortcomings issues. Having understood the merits and disadvantages of the "merging" scheme, we then introduce in Chapter 3 another approach to realize bifunctional metadevices under linearly polarized excitations, working in both reflection and transmission geometries or even in the full space. As a step further, we summarizes our efforts in Chapter 4 on making multifunctional devices under circularly polarized excitations, again including designing principles and devices fabrications/characterizations. Starting from Chapter 5, we turn to introduce our efforts on using the "active" scheme to construct multifunctional metadevices under linearly polarized wave operation. Chapter 6 further concentrates on how to employ the tunable strategy to achieve helicity/frequency controls of the circularly polarized waves in reflection geometry. We finally conclude this book in Chapter 7 by presenting our perspectives on future directions of metasurfaces and metadevices.
Book Synopsis Thermoelectric Nanomaterials by : Kunihito Koumoto
Download or read book Thermoelectric Nanomaterials written by Kunihito Koumoto and published by Springer Science & Business Media. This book was released on 2013-07-20 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presently, there is an intense race throughout the world to develop good enough thermoelectric materials which can be used in wide scale applications. This book focuses comprehensively on very recent up-to-date breakthroughs in thermoelectrics utilizing nanomaterials and methods based in nanoscience. Importantly, it provides the readers with methodology and concepts utilizing atomic scale and nanoscale materials design (such as superlattice structuring, atomic network structuring and properties control, electron correlation design, low dimensionality, nanostructuring, etc.). Furthermore, also indicates the applications of thermoelectrics expected for the large emerging energy market. This book has a wide appeal and application value for anyone being interested in state-of-the-art thermoelectrics and/or actual viable applications in nanotechnology.
