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The self-learning ability of Machine Learning algorithms makes the investigations more accurate and accommodates all the complex requirements. Development in neural codes can accommodate the data in all the forms such as numerical values as well as images. The techniques also review the sustainability, life-span, the energy consumption in production polymer, etc. This book addresses the design, characterization, and development of prediction analysis of sustainable polymer composites using Machine Learning algorithms.
Machine Learning, a substantial component of Artificial Intelligence (AI), is making rapid advancements and is an exciting pathway for AI’s contributions to the study of material science. Over the past decade, Machine Learning has experienced significant growth and is now making its way into scientific disciplines, including material science. Machine Learning aids in characterizing materials, predicting properties at the molecular level, expediting simulations, discovering new materials, and constructing models for designing novel materials