Enhancing Nanocrystal Synthesis: A Comparative Study of Online Artificial Intelligence Optimization and Offline High-Throughput Experimentation in Chemical Material Discovery

The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) has revolutionized chemical material discovery, accelerating the optimization process with an unprecedented efficiency and precision. This study conducts a detailed comparison of online AI and offline HTE approaches for chemical experimentation. Focusing on nanocrystal synthesis as an example application, online AI optimization is found to achieve superior performance and sustainability compared to conventional HTE methods that sweep larger experimental spaces. Specifically, for synthesizing NaYF4:Yb/Tm nanocrystals, offline HTE reaches better quality metrics, a good efficiency, and high carbon emissions, while online AI reaches similar quality and consumes far fewer raw materials with a better efficiency and low carbon emission. These findings spotlight AI's potential to enhance the precision of chemical experiments while reducing environmental impacts through sustainable management of resources.