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Data Analytics and What It Means to the Materials Community

Author: National Academies of Sciences Engineering and Medicine

Publisher:

Published: 2021-09-12

Total Pages:

ISBN-13: 9780309664080

Emerging techniques in data analytics, including machine learning and artificial intelligence, offer exciting opportunities for advancing scientific discovery and innovation in materials science. Vast repositories of experimental data and sophisticated simulations are being utilized to predict material properties, design and test new compositions, and accelerate nearly every facet of traditional materials science. How can the materials science community take advantage of these opportunities while avoiding potential pitfalls? What roadblocks may impede progress in the coming years, and how might they be addressed? To explore these issues, the Workshop on Data Analytics and What It Means to the Materials Community was organized as part of a workshop series on Defense Materials, Manufacturing, and Its Infrastructure. Hosted by the National Academies of Sciences, Engineering, and Medicine, the 2-day workshop was organized around three main topics: materials design, data curation, and emerging applications. Speakers identified promising data analytics tools and their achievements to date, as well as key challenges related to dealing with sparse data and filling data gaps; decisions around data storage, retention, and sharing; and the need to access, combine, and use data from disparate sources. Participants discussed the complementary roles of simulation and experimentation and explored the many opportunities for data informatics to increase the efficiency of materials discovery, design, and testing by reducing the amount of experimentation required. With an eye toward the ultimate goal of enabling applications, attendees considered how to ensure that the benefits of data analytics tools carry through the entire materials development process, from exploration to validation, manufacturing, and use. This publication summarizes the presentations and discussion of the workshop.

Data Analytics and What It Means to the Materials Community

Author:

Publisher:

Published: 2021

Total Pages: 83

ISBN-13: 9780309664097

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Challenges and Applications of Data Analytics in Social Perspectives

Author: Sathiyamoorthi, V.

Publisher: IGI Global

Published: 2020-12-04

Total Pages: 324

ISBN-13: 179982568X

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With exponentially increasing amounts of data accumulating in real-time, there is no reason why one should not turn data into a competitive advantage. While machine learning, driven by advancements in artificial intelligence, has made great strides, it has not been able to surpass a number of challenges that still prevail in the way of better success. Such limitations as the lack of better methods, deeper understanding of problems, and advanced tools are hindering progress. Challenges and Applications of Data Analytics in Social Perspectives provides innovative insights into the prevailing challenges in data analytics and its application on social media and focuses on various machine learning and deep learning techniques in improving practice and research. The content within this publication examines topics that include collaborative filtering, data visualization, and edge computing. It provides research ideal for data scientists, data analysts, IT specialists, website designers, e-commerce professionals, government officials, software engineers, social media analysts, industry professionals, academicians, researchers, and students.

Data Analytics

Author: Juan J. Cuadrado-Gallego

Publisher: Springer Nature

Published: 2023-11-30

Total Pages: 486

ISBN-13: 3031391292

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Building upon the knowledge introduced in The Data Science Framework, this book provides a comprehensive and detailed examination of each aspect of Data Analytics, both from a theoretical and practical standpoint. The book explains representative algorithms associated with different techniques, from their theoretical foundations to their implementation and use with software tools. Designed as a textbook for a Data Analytics Fundamentals course, it is divided into seven chapters to correspond with 16 weeks of lessons, including both theoretical and practical exercises. Each chapter is dedicated to a lesson, allowing readers to dive deep into each topic with detailed explanations and examples. Readers will learn the theoretical concepts and then immediately apply them to practical exercises to reinforce their knowledge. And in the lab sessions, readers will learn the ins and outs of the R environment and data science methodology to solve exercises with the R language. With detailed solutions provided for all examples and exercises, readers can use this book to study and master data analytics on their own. Whether you're a student, professional, or simply curious about data analytics, this book is a must-have for anyone looking to expand their knowledge in this exciting field.

Handbook of Materials Modeling

Author: Sidney Yip

Publisher: Springer Science & Business Media

Published: 2007-11-17

Total Pages: 2903

ISBN-13: 1402032862

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The first reference of its kind in the rapidly emerging field of computational approachs to materials research, this is a compendium of perspective-providing and topical articles written to inform students and non-specialists of the current status and capabilities of modelling and simulation. From the standpoint of methodology, the development follows a multiscale approach with emphasis on electronic-structure, atomistic, and mesoscale methods, as well as mathematical analysis and rate processes. Basic models are treated across traditional disciplines, not only in the discussion of methods but also in chapters on crystal defects, microstructure, fluids, polymers and soft matter. Written by authors who are actively participating in the current development, this collection of 150 articles has the breadth and depth to be a major contributor toward defining the field of computational materials. In addition, there are 40 commentaries by highly respected researchers, presenting various views that should interest the future generations of the community. Subject Editors: Martin Bazant, MIT; Bruce Boghosian, Tufts University; Richard Catlow, Royal Institution; Long-Qing Chen, Pennsylvania State University; William Curtin, Brown University; Tomas Diaz de la Rubia, Lawrence Livermore National Laboratory; Nicolas Hadjiconstantinou, MIT; Mark F. Horstemeyer, Mississippi State University; Efthimios Kaxiras, Harvard University; L. Mahadevan, Harvard University; Dimitrios Maroudas, University of Massachusetts; Nicola Marzari, MIT; Horia Metiu, University of California Santa Barbara; Gregory C. Rutledge, MIT; David J. Srolovitz, Princeton University; Bernhardt L. Trout, MIT; Dieter Wolf, Argonne National Laboratory.

Statistical Methods for Materials Science

Author: Jeffrey P. Simmons

Publisher: CRC Press

Published: 2019-02-13

Total Pages: 537

ISBN-13: 1498738214

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Data analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Additionally, the analysis and applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection.

Big Data in Materials Research and Development

Author: National Research Council

Publisher: National Academies Press

Published: 2014-10-22

Total Pages: 114

ISBN-13: 0309303826

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Big Data in Materials Research and Development is the summary of a workshop convened by the National Research Council Standing Committee on Defense Materials Manufacturing and Infrastructure in February 2014 to discuss the impact of big data on materials and manufacturing. The materials science community would benefit from appropriate access to data and metadata for materials development, processing, application development, and application life cycles. Currently, that access does not appear to be sufficiently widespread, and many workshop participants captured the constraints and identified potential improvements to enable broader access to materials and manufacturing data and metadata. This report discusses issues in defense materials, manufacturing and infrastructure, including data ownership and access; collaboration and exploitation of big data's capabilities; and maintenance of data.

Data Science for Undergraduates

Author: National Academies of Sciences, Engineering, and Medicine

Publisher: National Academies Press

Published: 2018-11-11

Total Pages: 139

ISBN-13: 0309475597

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Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.

Data-Based Methods for Materials Design and Discovery

Author: Ghanshyam Pilania

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 172

ISBN-13: 3031023838

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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.

Computational Materials System Design

Author: Dongwon Shin

Publisher: Springer

Published: 2017-11-10

Total Pages: 233

ISBN-13: 3319682806

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This book provides state-of-the-art computational approaches for accelerating materials discovery, synthesis, and processing using thermodynamics and kinetics. The authors deliver an overview of current practical computational tools for materials design in the field. They describe ways to integrate thermodynamics and kinetics and how the two can supplement each other.