Machine Learning In Materials Science
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Machine Learning In Materials Science
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Author : Keith T. Butler
language : en
Publisher: American Chemical Society
Release Date : 2022-06-16
Machine Learning In Materials Science written by Keith T. Butler and has been published by American Chemical Society this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-16 with Technology & Engineering categories.
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.
Machine Learning And Data Mining In Materials Science
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Author : Norbert Huber
language : en
Publisher: Frontiers Media SA
Release Date : 2020-04-22
Machine Learning And Data Mining In Materials Science written by Norbert Huber and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-22 with categories.
Materials Data Science
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Author : Stefan Sandfeld
language : en
Publisher: Springer Nature
Release Date : 2024-05-08
Materials Data Science written by Stefan Sandfeld and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-08 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.
Machine Learning For Materials Discovery
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Author : N. M. Anoop Krishnan
language : en
Publisher: Springer Nature
Release Date : 2024-05-06
Machine Learning For Materials Discovery written by N. M. Anoop Krishnan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-06 with Technology & Engineering categories.
Focusing on the fundamentals of machine learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspect—each method presented herein is accompanied by a code that implements the method in open-source platforms such as Python. This book is thus aimed at graduate students, researchers, and engineers to enable the use of data-driven methods for understanding and accelerating the discovery of novel materials.
Machine Learning In 2d Materials Science
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Author : Parvathi Chundi
language : en
Publisher: CRC Press
Release Date : 2023-11-13
Machine Learning In 2d Materials Science written by Parvathi Chundi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-13 with Computers categories.
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.
Machine Learning For Advanced Functional Materials
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Author : Nirav Joshi
language : en
Publisher: Springer Nature
Release Date : 2023-05-22
Machine Learning For Advanced Functional Materials written by Nirav Joshi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-22 with Science categories.
This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material’s electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.
Artificial Intelligence For Materials Science
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Author : Yuan Cheng
language : en
Publisher: Springer Nature
Release Date : 2021-03-26
Artificial Intelligence For Materials Science written by Yuan Cheng and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-26 with Technology & Engineering categories.
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.
Machine Learning For Materials Science
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Author : Bertrand Rouet-Leduc
language : en
Publisher:
Release Date : 2017
Machine Learning For Materials Science written by Bertrand Rouet-Leduc and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Manufacturing Engineering And Materials Science
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Author : Abhineet Saini
language : en
Publisher: CRC Press
Release Date : 2023-11-15
Manufacturing Engineering And Materials Science written by Abhineet Saini and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-15 with Technology & Engineering categories.
This book, which is part of a two-volume handbook set, gives a comprehensive description of recent developments in materials science and manufacturing technology, aiming primarily at its applications in biomedical science, advanced engineering materials, conventional/non-conventional manufacturing techniques, sustainable engineering design, and related domains. Manufacturing Engineering and Materials Science: Tools and Applications provides state-of-the-art research conducted in the fields of technological advancements in surface engineering, tribology, additive manufacturing, precision manufacturing, electromechanical systems, and computer-assisted design and manufacturing. The book captures emerging areas of materials science and advanced manufacturing engineering and presents the most recent trends in research for emerging researchers, field engineers, and academic professionals.
Machine Learning For Materials Discovery
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Author : N. M. Anoop Krishnan
language : en
Publisher: Springer
Release Date : 2025-05-08
Machine Learning For Materials Discovery written by N. M. Anoop Krishnan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-08 with Technology & Engineering categories.
Focusing on the fundamentals of machine learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspect—each method presented herein is accompanied by a code that implements the method in open-source platforms such as Python. This book is thus aimed at graduate students, researchers, and engineers to enable the use of data-driven methods for understanding and accelerating the discovery of novel materials.