Development of Smart Nanostructures for Energy Storage Using Artificial Intelligence: A Statistical Analysis Approach

The urgent need for efficient, eco-friendly energy storage solutions drives innovation in batteries and supercapacitors for electric vehicles and renewable energy systems. This study combines artificial intelligence (AI) and statistical analysis to design advanced nanomaterials, enhancing performance and sustainability. Using data from the Materials Project and lab experiments, we applied machine learning models like neural networks (R² = ۰.۹۲) and random forests (R² = ۰.۸۹) to predict properties such as battery capacity and conductivity. Statistical tools, including regression and variance analysis, confirmed these predictions, showing strong links between particle size and performance (p < ۰.۰۱). Our AI-designed nanostructures, such as carbon-coated LiFePO۴ and porous graphene, achieved a battery capacity of ۱۷۲ mAh/g and a power output ۲۵% higher than standard materials. These improvements, achieved with ۶۰% fewer experiments, promise faster, cost-effective research. Challenges like limited data and complex AI models highlight the need for better datasets and simpler algorithms. By promoting sustainable manufacturing, this work paves the way for greener energy storage, with future potential in AI-driven material discovery and eco-conscious production methods.

کلیدواژه ها:

Smart nanostructures ، energy storage ، artificial intelligence ، statistical analysis ، sustainability ، scalability