An Introduction To Materials Informatics


An Introduction To Materials Informatics
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An Introduction To Materials Informatics


An Introduction To Materials Informatics
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Author : Tongyi Zhang
language : en
Publisher: Springer
Release Date : 2024-02-20

An Introduction To Materials Informatics written by Tongyi Zhang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-20 with Technology & Engineering categories.


This textbook educates current and future materials workers, engineers, and researchers on Materials Informatics. Volume I serves as an introduction, merging AI, ML, materials science, and engineering. It covers essential topics and algorithms in 11 chapters, including Linear Regression, Neural Networks, and more. Suitable for diverse fields like materials science, physics, and chemistry, it enables quick and easy learning of Materials Informatics for readers without prior AI and ML knowledge.



Materials Science And Engineering


Materials Science And Engineering
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Author : Krishna Rajan
language : en
Publisher: Elsevier Inc. Chapters
Release Date : 2013-07-10

Materials Science And Engineering written by Krishna Rajan and has been published by Elsevier Inc. Chapters this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-10 with Technology & Engineering categories.


Studying structure–property relationships is an accepted paradigm in materials science, yet these relationships are often not linear and the challenge is to seek patterns among multiple length and time scales. There is rarely a single multiscale theory or experiment that can meaningfully and accurately capture such information. In this chapter we introduce the rationale as to why we need informatics by briefly summarizing the challenges of information complexity one has to deal with in material science, in order to systematically establish structure–property–processing relationships. Some of the concepts and topics to be covered in this book are introduced, including information networks, data mining, databases, and combinatorial experiments to mention a few. The value of “materials informatics” lies in its ability to permit one to survey complex, multiscale information in a high-throughput, statistically robust and yet physically meaningful manner.



Materials Informatics


Materials Informatics
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Author : Olexandr Isayev
language : en
Publisher: John Wiley & Sons
Release Date : 2019-12-04

Materials Informatics written by Olexandr Isayev and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-04 with Technology & Engineering categories.


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.



Materials Informatics And Catalysts Informatics


Materials Informatics And Catalysts Informatics
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Author : Keisuke Takahashi
language : en
Publisher: Springer Nature
Release Date :

Materials Informatics And Catalysts Informatics written by Keisuke Takahashi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.




Informatics For Materials Science And Engineering Data Driven Discovery For Accelerated Experimentation And Application


Informatics For Materials Science And Engineering Data Driven Discovery For Accelerated Experimentation And Application
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Author : Krishna Rajan
language : en
Publisher: Butterworth-Heinemann
Release Date : 2017-11-13

Informatics For Materials Science And Engineering Data Driven Discovery For Accelerated Experimentation And Application written by Krishna Rajan and has been published by Butterworth-Heinemann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-13 with Technology & Engineering categories.


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 "



Informatics For Materials Science And Engineering


Informatics For Materials Science And Engineering
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Author : Krishna Rajan
language : en
Publisher: Butterworth-Heinemann
Release Date : 2013-07-10

Informatics For Materials Science And Engineering written by Krishna Rajan and has been published by Butterworth-Heinemann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-10 with Technology & Engineering categories.


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



Materials Informatics Complete Self Assessment Guide


Materials Informatics Complete Self Assessment Guide
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Author : Gerardus Blokdyk
language : en
Publisher:
Release Date :

Materials Informatics Complete Self Assessment Guide written by Gerardus Blokdyk and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.




Materials Data Science


Materials Data Science
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Author : Stefan Sandfeld
language : en
Publisher: Springer
Release Date : 2023-12-05

Materials Data Science written by Stefan Sandfeld and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-05 with Technology & Engineering categories.


This text covers all of the data science, machine learning, and deep learning topics relevant to materials science and engineering, accompanied by numerous examples and applications. Almost all methods and algorithms introduced are implemented “from scratch” using Python and NumPy. The book starts with an introduction to statistics and probabilities, explaining important concepts such as random variables and probability distributions, Bayes’ theorem and correlations, sampling techniques, and exploratory data analysis, and puts them in the context of materials science and engineering. Therefore, it serves as a valuable primer for both undergraduate and graduate students, as well as a review for research scientists and practicing engineers. The second part provides an in-depth introduction of (statistical) machine learning. It begins with outlining fundamental concepts and proceeds to explore a variety of supervised learning techniques for regression and classification, including advanced methods such as kernel regression and support vector machines. The section on unsupervised learning emphasizes principal component analysis, and also covers manifold learning (t-SNE and UMAP) and clustering techniques. Additionally, feature engineering, feature importance, and cross-validation are introduced. The final part on neural networks and deep learning aims to promote an understanding of these methods and dispel misconceptions that they are a “black box”. The complexity gradually increases until fully connected networks can be implemented. Advanced techniques and network architectures, including GANs, are implemented “from scratch” using Python and NumPy, which facilitates a comprehensive understanding of all the details and enables the user to conduct their own experiments in Deep Learning.



Information Science For Materials Discovery And Design


Information Science For Materials Discovery And Design
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Author : Turab Lookman
language : en
Publisher: Springer
Release Date : 2019-03-27

Information Science For Materials Discovery And Design written by Turab Lookman and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-27 with Technology & Engineering categories.


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.



Hierarchical Materials Informatics


Hierarchical Materials Informatics
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Author : Surya R. Kalidindi
language : en
Publisher: Elsevier
Release Date : 2015-08-06

Hierarchical Materials Informatics written by Surya R. Kalidindi and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-06 with Technology & Engineering categories.


Custom design, manufacture, and deployment of new high performance materials for advanced technologies is critically dependent on the availability of invertible, high fidelity, structure-property-processing (SPP) linkages. Establishing these linkages presents a major challenge because of the need to cover unimaginably large dimensional spaces. Hierarchical Materials Informatics addresses objective, computationally efficient, mining of large ensembles of experimental and modeling datasets to extract this core materials knowledge. Furthermore, it aims to organize and present this high value knowledge in highly accessible forms to end users engaged in product design and design for manufacturing efforts. As such, this emerging field has a pivotal role in realizing the goals outlined in current strategic national initiatives such as the Materials Genome Initiative (MGI) and the Advanced Manufacturing Partnership (AMP). This book presents the foundational elements of this new discipline as it relates to the design, development, and deployment of hierarchical materials critical to advanced technologies. Addresses a critical gap in new materials research and development by presenting a rigorous statistical framework for the quantification of microstructure Contains several case studies illustrating the use of modern data analytic tools on microstructure datasets (both experimental and modeling)