Machine Learning In 2d Materials Science

Author : Parvathi Chundi
Publisher : CRC Press
ISBN 13 : 1000987434
Total Pages : 249 pages
Book Rating : 4.30/5 ( download)

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Download or read book Machine Learning in 2D Materials Science written by Parvathi Chundi and published by CRC Press. This book was released on 2023-11-13 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science and machine learning (ML) methods are increasingly being used to transform the way research is being conducted in materials science to enable new discoveries and design new materials. For any materials science researcher or student, it may be daunting to figure out if ML techniques are useful for them or, if so, which ones are applicable in their individual contexts, and how to study the effectiveness of these methods systematically. KEY FEATURES • Provides broad coverage of data science and ML fundamentals to materials science researchers so that they can confidently leverage these techniques in their research projects. • Offers introductory material in topics such as ML, data integration, and 2D materials. • Provides in-depth coverage of current ML methods for validating 2D materials using both experimental and simulation data, researching and discovering new 2D materials, and enhancing ML methods with physical properties of materials. • Discusses customized ML methods for 2D materials data and applications and high-throughput data acquisition. • Describes several case studies illustrating how ML approaches are currently leading innovations in the discovery, development, manufacturing, and deployment of 2D materials needed for strengthening industrial products. • Gives future trends in ML for 2D materials, explainable AI, and dealing with extremely large and small, diverse datasets. Aimed at materials science researchers, this book allows readers to quickly, yet thoroughly, learn the ML and AI concepts needed to ascertain the applicability of ML methods in their research.

Author : Keith T. Butler
Publisher : American Chemical Society
ISBN 13 : 0841299463
Total Pages : 176 pages
Book Rating : 4.67/5 ( download)

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Download or read book Machine Learning in Materials Science written by Keith T. Butler and published by American Chemical Society. This book was released on 2022-06-16 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Materials Science provides the fundamentals and useful insight into where Machine Learning (ML) will have the greatest impact for the materials science researcher. This digital primer provides example methods for ML applied to experiments and simulations, including the early stages of building an ML solution for a materials science problem, concentrating on where and how to get data and some of the considerations when choosing an approach. The authors demonstrate how to build more robust models, how to make sure that your colleagues trust the results, and how to use ML to accelerate or augment simulations, by introducing methods in which ML can be applied to analyze and process experimental data. They also cover how to build integrated closed-loop experiments where ML is used to plan the course of a materials optimization experiment and how ML can be utilized in the discovery of materials on computers.

Author : Abby L. Parrill
Publisher : John Wiley & Sons
ISBN 13 : 1119103932
Total Pages : 486 pages
Book Rating : 4.36/5 ( download)

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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

Author : Yuan Cheng
Publisher : Springer Nature
ISBN 13 : 3030683109
Total Pages : 231 pages
Book Rating : 4.08/5 ( download)

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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.

Author : Eui-Hyeok Yang
Publisher : Elsevier
ISBN 13 : 0128184760
Total Pages : 502 pages
Book Rating : 4.69/5 ( download)

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Download or read book Synthesis, Modelling and Characterization of 2D Materials and their Heterostructures written by Eui-Hyeok Yang and published by Elsevier. This book was released on 2020-06-19 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: Synthesis, Modelling and Characterization of 2D Materials and Their Heterostructures provides a detailed discussion on the multiscale computational approach surrounding atomic, molecular and atomic-informed continuum models. In addition to a detailed theoretical description, this book provides example problems, sample code/script, and a discussion on how theoretical analysis provides insight into optimal experimental design. Furthermore, the book addresses the growth mechanism of these 2D materials, the formation of defects, and different lattice mismatch and interlayer interactions. Sections cover direct band gap, Raman scattering, extraordinary strong light matter interaction, layer dependent photoluminescence, and other physical properties. Explains multiscale computational techniques, from atomic to continuum scale, covering different time and length scales Provides fundamental theoretical insights, example problems, sample code and exercise problems Outlines major characterization and synthesis methods for different types of 2D materials

Author : N. M. Anoop Krishnan
Publisher : Springer Nature
ISBN 13 : 3031446224
Total Pages : 287 pages
Book Rating : 4.21/5 ( download)

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Download or read book Machine Learning for Materials Discovery written by N. M. Anoop Krishnan and published by Springer Nature. This book was released on with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Sigrid D. Peyerimhoff
Publisher : John Wiley & Sons
ISBN 13 : 9783527277322
Total Pages : 380 pages
Book Rating : 4.23/5 ( download)

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Download or read book Interactions in Molecules written by Sigrid D. Peyerimhoff and published by John Wiley & Sons. This book was released on 2003-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experimentalists and theoreticians from chemistry and physics present various interactions in molecules using methods of chemical synthesis, structural analysis, spectroscopy and quantum chemical computations. This work constitutes an important basis in the investigation of increasingly more complex systems such as the study of the action of drugs in pharmaceutical research. The book gives an excellent survey for the specialist and also a welcome introduction for the advanced graduate student and the researcher in neighboring fields.

Author : Rajesh Jha
Publisher : CRC Press
ISBN 13 : 1000541339
Total Pages : 363 pages
Book Rating : 4.35/5 ( download)

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Download or read book Artificial Intelligence-Aided Materials Design written by Rajesh Jha and published by CRC Press. This book was released on 2022-03-15 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the application of artificial intelligence (AI)/machine learning (ML) concepts to develop predictive models that can be used to design alloy materials, including hard and soft magnetic alloys, nickel-base superalloys, titanium-base alloys, and aluminum-base alloys. Readers new to AI/ML algorithms can use this book as a starting point and use the MATLAB® and Python implementation of AI/ML algorithms through included case studies. Experienced AI/ML researchers who want to try new algorithms can use this book and study the case studies for reference. Offers advantages and limitations of several AI concepts and their proper implementation in various data types generated through experiments and computer simulations and from industries in different file formats Helps readers to develop predictive models through AI/ML algorithms by writing their own computer code or using resources where they do not have to write code Covers downloadable resources such as MATLAB GUI/APP and Python implementation that can be used on common mobile devices Discusses the CALPHAD approach and ways to use data generated from it Features a chapter on metallurgical/materials concepts to help readers understand the case studies and thus proper implementation of AI/ML algorithms under the framework of data-driven materials science Uses case studies to examine the importance of using unsupervised machine learning algorithms in determining patterns in datasets This book is written for materials scientists and metallurgists interested in the application of AI, ML, and data science in the development of new materials.

Author : Vinod Kushvaha
Publisher : Springer Nature
ISBN 13 : 9811962782
Total Pages : 202 pages
Book Rating : 4.83/5 ( download)

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Download or read book Machine Learning Applied to Composite Materials written by Vinod Kushvaha and published by Springer Nature. This book was released on 2022-11-29 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the approach of Machine Learning (ML) based predictive models in the design of composite materials to achieve the required properties for certain applications. ML can learn from existing experimental data obtained from very limited number of experiments and subsequently can be trained to find solutions of the complex non-linear, multi-dimensional functional relationships without any prior assumptions about their nature. In this case the ML models can learn from existing experimental data obtained from (1) composite design based on various properties of the matrix material and fillers/reinforcements (2) material processing during fabrication (3) property relationships. Modelling of these relationships using ML methods significantly reduce the experimental work involved in designing new composites, and therefore offer a new avenue for material design and properties. The book caters to students, academics and researchers who are interested in the field of material composite modelling and design.

Author : Inamuddin
Publisher : Materials Research Forum LLC
ISBN 13 : 1644902532
Total Pages : 147 pages
Book Rating : 4.30/5 ( download)

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Download or read book Application of Artificial Intelligence in New Materials Discovery written by Inamuddin and published by Materials Research Forum LLC. This book was released on 2023-07-05 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is concerned with the use of Artificial Intelligence in the discovery, production and application of new engineering materials. Topics covered include nano-robots. data mining, solar energy systems, materials genomics, polymer manufacturing, and energy conversion issues. Keywords: Artificial Intelligence, Mathematical Models, Machine Learning, Artificial Neural Networks, Bayesian Analysis, Vector Machines, Heuristics, Crystal Structure, Component Prediction, Process Optimization, Density Functional Theory, Monitoring, Classification, Nano-Robots, Data Mining, Solar Photovoltaics, Renewable Energy Systems, Alternative Energy Sources, Material Genomics, Polymer Manufacturing, Energy Conversion.