Sign-In
Cart
AI for Materials Science
Paperback
Please select the version of book you would like to purchase.
ISBN13: 9798265147981
Publisher: Independently Published
Published: Sep 13 2025
Pages: 240
Weight: 0.72
Height: 0.51 Width: 6.00 Depth: 9.00
Language: English
Publisher: Independently Published
Published: Sep 13 2025
Pages: 240
Weight: 0.72
Height: 0.51 Width: 6.00 Depth: 9.00
Language: English
AI for Materials Science is a comprehensive and accessible textbook designed for undergraduate (B.Tech) and postgraduate (M.Tech) students of engineering and science. In an era where data has become the most valuable resource, this book provides a roadmap for leveraging Artificial Intelligence and Machine Learning to revolutionize the field of materials research and development. It systematically bridges the gap between the principles of materials science and the powerful algorithms of AI, equipping students with the knowledge and practical skills needed to design the materials of the future. Key Features of the Book: 1. Beginner to Advanced Coverage: The book follows a logical progression, starting with fundamental concepts for absolute beginners and gradually building up to advanced, state-of-the-art topics, making it suitable for a wide range of learners.
2. Practical, Hands-On Approach: Heavy emphasis is placed on learning by doing. The book is packed with hands-on coding exercises, practicals, and mini-projects that reinforce theoretical concepts.
3. Interdisciplinary Focus: It is carefully designed to be accessible to students from various branches of engineering, including Materials, Mechanical, Chemical, and Computer Science, by providing the necessary background for each domain.
4. Capstone Project-Based Learning: The final chapter is a comprehensive, live capstone project that integrates all the concepts learned throughout the book. It includes a complete, well-explained codebase for solving a real-world material design problem.
5. Modular and Structured: With 10 well-defined chapters, the book can be easily adapted for a one-semester course. Each chapter includes learning objectives, summaries, and review questions to aid in structured learning.
6. Industry-Relevant Case Studies: Features contemporary case studies on high-impact areas like renewable energy (solar cells, batteries), sustainable materials, high-performance alloys, and drug delivery systems.
7. Focus on Open-Source Tools: Empowers students by teaching them to use powerful, industry-standard, and freely available Python libraries for data analysis, machine learning, and materials informatics. The book's core objective is to move beyond theoretical discussions and provide a hands-on learning experience. It guides the reader from the foundational concepts of both AI and materials science to the application of sophisticated algorithms for predicting material properties, discovering new compounds, and optimizing manufacturing processes. The content is presented in a lucid, step-by-step manner, making complex topics like deep learning, generative models, and graph neural networks intuitive and understandable.
3. Interdisciplinary Focus: It is carefully designed to be accessible to students from various branches of engineering, including Materials, Mechanical, Chemical, and Computer Science, by providing the necessary background for each domain.
4. Capstone Project-Based Learning: The final chapter is a comprehensive, live capstone project that integrates all the concepts learned throughout the book. It includes a complete, well-explained codebase for solving a real-world material design problem.
5. Modular and Structured: With 10 well-defined chapters, the book can be easily adapted for a one-semester course. Each chapter includes learning objectives, summaries, and review questions to aid in structured learning.
6. Industry-Relevant Case Studies: Features contemporary case studies on high-impact areas like renewable energy (solar cells, batteries), sustainable materials, high-performance alloys, and drug delivery systems.
7. Focus on Open-Source Tools: Empowers students by teaching them to use powerful, industry-standard, and freely available Python libraries for data analysis, machine learning, and materials informatics. The book's core objective is to move beyond theoretical discussions and provide a hands-on learning experience. It guides the reader from the foundational concepts of both AI and materials science to the application of sophisticated algorithms for predicting material properties, discovering new compounds, and optimizing manufacturing processes. The content is presented in a lucid, step-by-step manner, making complex topics like deep learning, generative models, and graph neural networks intuitive and understandable.
Also from
Singh, Ajit
Object Oriented Programming With C++: Simply In Depth
Singh, Ajit
Kumari, Renu
Prasad, Manisha
Paperback
$19.80
Advanced Web Technologies Simply In Depth: Servlet, JSP, Web Services, C#, ASP .NET, XML, AJAX
Kumar, Praveen
Singh, Ajit
Paperback
$17.16
The Generative AI Stack: Building Real-World Applications with LLMs and Diffusion Models
Singh, Ajit
Paperback
$34.51