To fully visualise the intricate interactions among the mechanical, chemical and physical characteristics of materials sharing the same chemical formula, this project employs advanced data analytics in conjunction with a Power BI dashboard. Although these materials possess identical chemical compositions, variations in structural arrangements and manufacturing processes generate significant differences in their measurable properties. Such distinctions directly influence their functional performance and industrial applicability. This research systematically analyses variations in key attributes, including density, volume and band gap energy, through the application of advanced data mining algorithms. These computational methods are integrated into an interactive Power BI dashboard designed to facilitate intuitive and dynamic data exploration. By combining statistical modelling with visual analytics, the study establishes a structured comparative framework that reveals relationships between processing techniques, structural configurations and resulting performance outcomes. The findings indicate that even minor alterations in structural arrangement can produce substantial changes in material behaviour. Variations in band gap energy, for example, significantly affect electronic and optical properties, which are fundamental to semiconductor technologies. Similarly, differences in density and volume influence mechanical strength, thermal stability and durability, all of which are critical considerations in aerospace and automotive engineering. These results emphasise the importance of adopting a targeted, evidence-based approach to material selection and optimisation. Beyond functioning as a visual representation tool, the Power BI dashboard operates as a practical decision-support system. It enables dynamic filtering, real-time comparison and cross-sectional analysis, allowing researchers and industry professionals to derive insights tailored to specific technical requirements. This interactive capability enhances analytical efficiency while transforming complex datasets into accessible, actionable knowledge. By converting raw scientific data into structured visual intelligence, the project bridges the gap between theoretical material science and real-world engineering application. The integration of analytics and dashboard technology accelerates the identification of optimal material configurations, reduces experimental inefficiencies and supports informed strategic decision-making. Ultimately, this research contributes to the advancement of material science by demonstrating how data-driven methodologies can expand academic understanding, strengthen industrial innovation and improve performance outcomes across high-precision technological sectors.
Rana et al. (Wed,) studied this question.