Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Jain, Rituraj; Singh, Sitesh Kumar; Upreti, Kamal; Bekuma, Yohannes Title A name given to the resource Artificial Intelligence and Machine Learning in Advanced Materials Science: A New Era of Innovation Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Engineering Solutions for Modern Challenges in Advanced Materials Science;pp.1-34 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-3556-8.ch001" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-3556-8.ch001</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033082624?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033082624?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jain R., Department of Information Technology, Marwadi University, Rajkot, Gujarat, India; Singh S.K., National University of Science and Technology, Oman; Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Ghaziabad, India; Bekuma Y., Wollega University, Ethiopia Description An account of the resource The increase of artificial intelligence (AI) and machine learning (ML) in the discovery, design, and optimization of new materials is causing a rapid acceleration of the field of materials science. This chapter addresses the principles for how artificial intelligence and machine learning can enable the predictive modeling, high-throughput screening, and smartproduction ofpolymers, alloys, ceramics, and nanomaterials. Emphasized are some of the techniques, such as hybrid approaches to artificial intelligence and physics, generative models, and reinforcement learning. Some important problems in the chapter are spoken about: lack of standards, incoherence of data, and unfeasibility of explaining the models. Along with that, this also attempts to investigate how the advantages of upcoming malware such as quantum computing, edge synthetic intelligence, and open information facilities can reinforce the research and innovation in the forthcoming age of materials. 2026 by IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833733558-2; 979-833733556-8; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online