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Advancing Material Property Prediction With AI and Deep Learning Technologies
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Author(s): Durga Prasad Garapati (Department of Artificial Intelligence, Shri Vishnu Engineering College for Women, India), S. Iniyan (Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Chennai, India), Sultanuddin SJ (Department of Cyber Security, Dhanalakshmi College of Engineering, Chennai, India), R. Manivannan (Department of Electrical and Electronics Engineering, St. Joseph's Institute of Technology, Chennai, India)and N. Pragadish (Department of Mechanical Engineering, Rajalakshmi Institute of Technology, Chennai, India)
Copyright: 2025
Pages: 26
Source title:
Using Computational Intelligence for Sustainable Manufacturing of Advanced Materials
Source Author(s)/Editor(s): Kamalakanta Muduli (Papua New Guinea University of Technology, Papua New Guinea), Bikash Ranjan Moharana (Papua New Guinea University of Technology, Papua New Guinea), Steve Korakan Ales (Papua New Guinea University of Technology, Papua New Guinea)and Dillip Kumar Biswal (Aryan Institute of Engineering and Technology, Bhubaneswar, India)
DOI: 10.4018/979-8-3693-7974-5.ch014
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Abstract
Artificial intelligence and deep learning technologies are driving transformations in material science, particularly in predicting material property through machine learning applications. In this chapter, we are concerned about how materials property prediction can be revolutionized by advances in new state-of-the-art technologies enabling more accurate and efficient predictions in comparison to the traditional methods which incur time-consuming and cost-consuming expense. While both ML and deep learning offer powerful alternatives, they require large datasets and robust algorithms to accurately map complex interplay between the material structure and its properties. In this text you will read about employing deep learning architectures, such as CNNs and RNNs, in predicting properties such as mechanical strength, thermal conductivity and electronic behavior. The chapter discusses successful case studies and predicts future material science research and industry practices integrating AI-driven approaches.
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