NANOTECHNOLOGY IN CONDENSED MATTER PHYSICS – IMPLICATIONS FOR MATERIAL SCIENCE AND ENGINEERING

Objectives: This research investigate the use of nanotechnology in condensed matter physics and become a reality in material science and engineering. It is particularly trying to predict the tensile strength of nanomaterials using machine learning (ML) algorithms. Its aims are to develop predictive models, explore the role of features, and evaluate the accuracy of certain ML algorithms. Methodology: The research seeks to investigate the use of nanotechnology in condensed matter physics and its translation to materials science and engineering. Of particular interest is the use of machine learning (ML) algorithms to predict the tensile strength of nanomaterials. The research goals are to construct predictive models, investigate feature importance, and determine the accuracy of various ML algorithms. Key Findings: The research validated that ensemble models, viz., Random Forest and XGBoost, outperformed linear models in predicting tensile strength. Material properties of greatest significance were Brinell Hardness (Bhn) and Shear Modulus (G). The research validated that prediction using ML can be an excellent alternative to traditional experimental protocols and can be used for rapid and inexpensive measurement of material properties. Implications: The results establish the potential of ML in materials science through reduction of dependency on labor-intensive and costly experimental testing. When integrated with AI-driven models, it can accelerate material innovation, and this will induce industries such as aerospace, biomedical engineering, and energy storage to maximize material selection and performance. Specific Contribution: This research contributes to nanotechnology and computational materials science by introducing an ML-based predictive model to predict tensile strength. It fills the gap between experiment and computation by a comprehensive study of machine learning models for material property prediction.