Abstract
In the face of the pressing climate change crisis, Molecular Solar Thermal Energy Storage (MOST) Systems offer a promising avenue for efficient energy storage. This study focuses on the potential of systems based on azobenzene and gives a comprehensive framework for assessing unique azobenzene variations for MOST applications. A high-throughput screening process, underpinned by semi-empirical extended tight binding methods, has been developed to enable exploration of the vast chemical space of azobenzenes. The codebase for the established screening procedure, including methodologies and tools, is organized and shared through a GitHub repository ensuring transparency and reproducibility. We test our high throughput screening procedure on 37,729 azobenzene derivatives and highlight that it is robust enough to facilitate subsequent studies that will dive deeper into the potential of azobenzenes in MOST applications. Future endeavors will focus on expanding the dataset, correlating energies with higher-level calculations, and harnessing advanced statistical and machine learning techniques to optimize the selection and performance of azobenzenes in MOST systems.