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Machine Learning for Materials Discovery : Numerical Recipes and Practical Applications / by N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo.
Springer Nature - Springer Physics and Astronomy eBooks 2024 English International Available online
View online- Format:
- Book
- Author/Creator:
- Krishnan, N. M. Anoop, author.
- Kodamana, Hariprasad, author.
- Bhattoo, Ravinder, author.
- Series:
- Machine Intelligence for Materials Science, 2948-1821
- Language:
- English
- Subjects (All):
- Materials science--Data processing.
- Materials science.
- Machine learning.
- System theory.
- Mathematical physics.
- Ceramic materials.
- Computational Materials Science.
- Machine Learning.
- Complex Systems.
- Theoretical, Mathematical and Computational Physics.
- Ceramics.
- Local Subjects:
- Computational Materials Science.
- Machine Learning.
- Complex Systems.
- Theoretical, Mathematical and Computational Physics.
- Ceramics.
- Physical Description:
- 1 online resource (287 pages)
- Edition:
- 1st ed. 2024.
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2024.
- Summary:
- 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.
- Contents:
- Part I: Introduction
- Part II: Basics of Machine Learning Methods
- Introduction to Data-Based Modeling
- Model Development
- Introduction to Machine Learning
- Quick Dive into Probabilistic Methods
- Optimization
- Part III: Application in Glass Science
- Property Prediction
- Glass Discovery
- Understanding Glass Physics
- Atomistic Modeling
- Future Directions.
- Notes:
- Includes bibliographical references and index.
- ISBN:
- 3-031-44622-4
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