Optimizing the performance of phase-change azobenzene: from trial and error to machine learning

Molecular solar thermal (MOST) systems employ molecular photoswitches to store or release solar energy as heat under specific conditions. Among these systems, azobenzene (Azo) derivatives are particularly noteworthy owing to their superior photoresponsive properties and efficient reversible isomerization. Recent pioneering studies have focused on the molecular design of Azo derivatives to enhance the energy storage performance. Phase-change Azo (PC-Azo) derivatives-fabricated by integrating molecular photoswitches with phase-change materials-capture both the enthalpy of phase change and that of isomerization, thereby considerably increasing the energy storage density of Azo. This research provides a review of the crucial performance parameters of PC-Azo-based MOST systems and delves into the factors influencing the phase change and isomerization of PC-Azo derivatives to provide guidelines for the application of machine learning in MOST systems. A key focus of the research is the application of theoretical computing and machine learning in recent molecular design advancements. The study emphasizes the importance of employing machine learning techniques in the molecular design of PC-Azo derivatives, underlining their potential in facilitating the targeted design of photothermal energy storage materials. This approach marks a significant stride in the field, offering innovative avenues for the development of advanced energy storage solutions. Machine learning can predict the properties of phase change azobenzene derivatives and guide molecular design to further improve their photothermal conversion performance.