Overview

Artificial intelligence (AI) rapidly transforms materials science by reshaping how materials are discovered and designed. Traditional approaches complement data methods that analyze vast experimental datasets with speed and accuracy. Through techniques like machine learning and generative modeling, AI enables the prediction of material properties and discovery of novel materials for energy and structural applications. As a result, the integration of AI into materials science reduces development time and cost while opening new paths toward sustainable and high-performance material systems. Applications of AI in Materials Science explores AI in materials engineering. It examines deep learning algorithms for materials science applications. Covering topics such as machine learning, nanotechnology, and biomaterials, this book is an excellent resource for academicians, researchers, software developers, engineers, and data scientists.

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Shahriar Sajib has a Bachelor of Computer Science, the Masters in International Business, Professional Accounting and Business Administration. He did his PhD on strategic management. Sajib has been teaching at University of Technology Sydney for more than 10 years and taught many subjects in business. He has observed the role of moral capital during his PhD data collection. He has published on dynamic capabilities, microfoundation, social license to operate, supply chain management and artificial intelligence in reputed journals such as IJIM, GJIM, IJOPM, JBR, Annals of Operations research etc. His 20 years of work experience and specially experience working in Grameen Bank, Bangladesh has allowed him a deep and rich insight about the role of ethical approaches of doing business or moral capital with value creation.

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