IJIRIS: International Journal of Innovative Research in Information Security E-ISSN: 2349-7017 Volume 10, Issue 04, May 2024 P-ISSN: 2349-7009 https://www.ijiris.com/archives ___________________________________________________________________________________ IJIRIS © 2014-24, AM Publications - All Rights Reserved https://doi.org/10.26562/ijiris Page -1 Material Property Prediction Using Machine Learning Neha Harde* Department of Computer Science Engineering East Point College of Engineering and Technology, Bengaluru, Karnataka, INDIA neha.cse@eastpoint.ac.in Madhushree R Department of Computer Science Engineering East Point College of Engineering and Technology, Bengaluru, Karnataka, INDIA madhushree.r@eastpoint.ac.in Publication History Manuscript Reference No: IJIRIS/RS/Vol.10/Issue04/MYIS10099 Research Article | Open Access | Double-Blind Peer-Reviewed | Article ID: IJIRIS/RS/Vol.10/Issue04/MYIS10099 Received: 07, April 2024|Revised:16, April 2024 | Accepted: 27, April 2024 Published Online: 07, May 2024 Volume 2024 Article ID MYIS10099 http://www.ijiris.com/volumes/Vol10/iss-04/20.MYIS10099.pdf Article Citation: Divya,Shounak,Shashi,Shravani(2024). Implementation of Vehicle Detection and Tracking Model Using YOLO and DeepSORT for Controlling Traffic Rule Violation. International Journal of Innovative Research in Information Security, Volume 10, Issue 04, Pages 210-214 doi:> https://doi.org/10.26562/ijiris.2024.v1004.20 BibTex key: Divya@2024Implementation Copyright: ©2024 This is an open access article distributed under the terms of the Creative Commons Attribution License; which Permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract: The ability to accurately predict the properties of materials is crucial for numerous applications across various industries, including materials science, engineering, and manufacturing. With the advent of machine learning (ML) techniques, researchers have gained powerful tools to model and predict material properties based on their composition, structure, and processing conditions. This review paper provides a comprehensive overview of material property prediction using machine learning. It covers the historical development, available ML models, recent trends, and prospects in this rapidly evolving domain. Keywords: Materials Informatics, Property Prediction, Data-Driven Materials Science, Computational Materials Design I. INTRODUCTION Understanding and predicting the properties of materials is a fundamental challenge in materials science and engineering. Traditional approaches, such as experimental characterization and computational simulations, can be time-consuming, resource-intensive, and often limited in their ability to explore the vast material design space. Machine learning has emerged as a powerful alternative, offering data-driven approaches to model and predict material properties with high accuracy and efficiency. Machine learning algorithms leverage large datasets of material properties and their corresponding descriptors (e.g., composition, structure, processing conditions) to build predictive models. These models can then be used to make accurate property predictions for new materials without the need for extensive experimental or computational efforts. The applicability of ML in material property prediction spans a wide range of properties, including mechanical, thermal, optical, electronic, and catalytic properties, among others. The field of material science and engineering has long been characterized by the quest for understanding and controlling the properties of materials to meet specific performance requirements in various applications. Traditionally, this process has relied heavily on empirical observations, theoretical models, and experimental testing. However, the increasing complexity of materials and the growing demand for novel materials with tailored properties have spurred the exploration of alternative approaches. In recent years, machine learning (ML) techniques have emerged as powerful tools for predicting material properties, offering new avenues for accelerating the materials discovery and development process. This paper reviews the current state-of- the-art in applying ML for predicting various material properties, including mechanical, electronic, optical, and thermal properties. The strengths and limitations of different ML approaches are discussed, along with future research directions. A. Significance of Material Property Prediction: Accurate prediction of material properties is crucial for accelerating the design and optimization of materials for diverse applications, including aerospace, automotive, electronics, energy, healthcare, and beyond. By accurately predicting material behaviors such as mechanical strength, thermal conductivity, electrical conductivity, corrosion resistance, and more, researchers and engineers can expedite the development of innovative materials with enhanced performance