Predicting Material Properties Using Machine Learning for Accelerated Materials Discovery

Abstract

The rapid prediction of material properties has become a pivotal factor in accelerating materials discovery and development, driven by advancements in machine learning and data-driven methodologies. This paper presents a novel system for predicting material properties using machine learning techniques, offering a scalable and efficient framework for exploring new materials with optimized properties. The system incorporates large datasets, feature engineering, and multiple machine learning models, such as Kernel Ridge Regression, Random Forest, and Neural Networks, to predict material properties like thermal conductivity, elastic modulus, and electronic bandgap. By integrating physics-based knowledge into machine learning models, the proposed system enhances the accuracy and interpretability of predictions. The results indicate that the system can significantly reduce the time and cost of material discovery while delivering high prediction accuracy. This is the potential approach to revolutionize materials science by enabling researchers to identify promising material candidates in silico, paving the way for breakthroughs in energy, electronics, and sustainable materials.