EXTENDED ABSTRACT: This presentation showcases an AI-driven platform for high-throughput nanoparticle synthesis and characterization, merging physical principles with AI models like genetic algorithms and deep learning. The platform streamlines production, cutting time and cost, while enabling researchers to conduct remote experiments with greater accuracy. Automated processes, such as liquid injections, stirring, coating, optical testing, and data storage, reduce human error and enhance precision. This advancement propels nanotechnology research and spurs innovation across industries, particularly in advanced coating materials. A notable application is in Transparent Solar Heat Control (TSHC) coatings for windows, essential for passive cooling in green buildings. We introduce a data-driven tandem neural networks model for TSHC coatings, achieving an R² value above 0.95, which accelerates development and ensures precise design. The TSHC coating, made of cesium tungsten oxide, antinomy tin oxide, and indium tin oxide nanoparticles, offers a luminous transmittance of 69%, UV transmittance of 0.1%, and NIR transmittance of 4%. Temperature tests reveal a potential indoor cooling of up to 8°C. This work presents a novel strategy for optimizing coatings with multiple functional compositions, underscoring the platform's potential to drive signiffcant advancements in sustainable building technologies.

Keywords: AI; laboratory management; automated platform for high-throughput characterization and synthesis. coating, green building, data-driven, inverse design

REFERENCES:

Zheng, Long, Teng Xiong, and Kwok Wei Shah, Solar Energy, 193, (2019), 837-858 [1].