Machine Learning Techniques for Predicting Conductive Properties of New Materials

Naveen Kumar Thawait and Umakant Shrivastava

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 3, 576-585

Abstract: The study "Machine Learning Techniques for Predicting Conductive Properties of New Materials" explores the application of advanced machine learning (ML) algorithms to predict the conductive properties of novel materials, accelerating the discovery and development process in materials science. Traditional methods for assessing conductive properties are often time-consuming and expensive, necessitating a more efficient approach. This research leverages various ML techniques, including supervised learning algorithms such as support vector machines, decision trees, and neural networks, to analyze large datasets of material properties and predict conductivity with high accuracy. Feature selection and engineering processes are employed to identify the most significant attributes influencing conductivity. The study also compares the performance of different ML models, optimizing hyperparameters to enhance prediction reliability. Results demonstrate that ML models can significantly reduce the experimental burden, offering rapid and precise predictions that align closely with empirical data. The integration of ML in materials science presents a transformative approach, enabling faster identification of promising conductive materials, thereby fostering advancements in electronics, energy storage, and other technological domains. The study highlights the potential of ML to revolutionize material property prediction, paving the way for accelerated innovation and application in various industries.

Keywords: Machine Learning; Conductive Properties; New Materials; Predictive Modeling; Supervised Learning; Feature Selection (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410340
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i3:id:235

DOI: 10.32628/CSEIT2410340

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