Machine Learning In Materials Informatics
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Materials Informatics
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Author : Olexandr Isayev
language : en
Publisher: John Wiley & Sons
Release Date : 2019-08-14
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 2019-08-14 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.
Machine Learning In Materials Informatics
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Author : Yuling An
language : en
Publisher:
Release Date : 2022
Machine Learning In Materials Informatics written by Yuling An and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Machine learning categories.
"This book is about machine learning in materials informatics"--
Artificial Intelligence For Materials Informatics
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Author : S. Sachin Kumar
language : en
Publisher: Springer Nature
Release Date : 2025-07-29
Artificial Intelligence For Materials Informatics written by S. Sachin Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-29 with Computers categories.
This comprehensive book explores the transformative impact of AI on materials informatics, delving into machine learning/deep learning, and material knowledge representation. Embracing the transformative power of artificial intelligence (AI), the field of materials informatics has witnessed a remarkable revolution in its methodology and applications. AI has revolutionized the field of materials informatics, enabling researchers to discover, design, and optimize materials with enhanced properties at an accelerated pace. It showcases how AI is accelerating materials discovery, property prediction, providing case studies, and a comprehensive bibliography for further exploration. This essential resource equips researchers, scientists, and engineers with the knowledge and tools to harness the power of AI for groundbreaking advancements in materials science.
An Introduction To Materials Informatics
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Author : Tongyi Zhang
language : en
Publisher: Springer Nature
Release Date : 2025-02-26
An Introduction To Materials Informatics written by Tongyi Zhang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-26 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 Informatics I
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Author : Kunal Roy
language : en
Publisher: Springer Nature
Release Date : 2025-05-10
Materials Informatics I written by Kunal Roy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-10 with Science categories.
This contributed volume explores the integration of machine learning and cheminformatics within materials science, focusing on predictive modeling techniques. It begins with foundational concepts in materials informatics and cheminformatics, emphasizing quantitative structure-property relationships (QSPR). The volume then presents various methods and tools, including advanced QSPR models, quantitative read-across structure-property relationship (q-RASPR) models, optimization strategies with minimal data, and in silico studies using different descriptors. Additionally, it explores machine learning algorithms and their applications in materials science, alongside innovative modeling approaches for quantum-theoretic properties. Overall, the book serves as a comprehensive resource for understanding and applying machine learning in the study and development of advanced materials and is a useful tool for students, researchers and professionals working in these areas.
Materials Informatics Ii
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Author : Kunal Roy
language : en
Publisher: Springer Nature
Release Date : 2025-03-14
Materials Informatics Ii written by Kunal Roy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-14 with Science categories.
This contributed volume explores the application of machine learning in predictive modeling within the fields of materials science, nanotechnology, and cheminformatics. It covers a range of topics, including electronic properties of metal nanoclusters, carbon quantum dots, toxicity assessments of nanomaterials, and predictive modeling for fullerenes and perovskite materials. Additionally, the book discusses multiscale modeling and advanced decision support systems for nanomaterial risk management, while also highlighting various machine learning tools, databases, and web platforms designed to predict the properties of materials and molecules. It is a comprehensive guide and a great tool for researchers working at the intersection of machine learning and material sciences.
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.
Materials Informatics Iii
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Author : Kunal Roy
language : en
Publisher: Springer Nature
Release Date : 2025-03-01
Materials Informatics Iii written by Kunal Roy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-01 with Science categories.
This contributed volume focuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials. It provides an in-depth look at how machine learning is utilized to predict key properties of polymers, deep eutectic solvents, and ionic liquids, as well as to improve safety and performance in the study of energetic and reactive materials. With chapters covering polymer informatics, quantitative structure–property relationship (QSPR) modeling, and computational approaches, the book serves as a comprehensive resource for researchers applying predictive modeling techniques to advance materials science and improve material safety and performance.
Homology And Machine Learning For Materials Informatics
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Author : Bart Olsthoorn
language : en
Publisher:
Release Date : 2023
Homology And Machine Learning For Materials Informatics written by Bart Olsthoorn and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
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.