SUN’IY INTELLEKT YORDAMIDA NANOBO‘YOQLAR TARKIBINI OPTIMALLASHTIRISH VA ULARNING OPTIK XOSSALARINI BASHORAT QILISH

This article examines the application of artificial intelligence technologies for optimizing the composition of nanopaints and predicting their optical properties. The influence of factors such as particle size, concentration, dispersion level, and component composition on optical performance is analyzed. Machine learning algorithms are employed to process experimental data, determine optimal formulations, and accurately predict optical characteristics including light absorption, reflection, and transmission. The findings demonstrate that artificial intelligence-based models can significantly accelerate the development of nanomaterials, reduce experimental costs, and improve the efficiency of designing high-performance nanopaints with enhanced optical properties.