Automated Synthesis for Closed-Loop Discovery of Photostable Light-Harvesting Molecules

Jan 25, 2024, 4:00 pm5:00 pm



Event Description

AI-guided closed-loop experimentation has recently emerged as a promising method to optimize objective functions. However, achieving the full potential of this approach in the chemical sciences requires new methods to efficiently access large chemical spaces. In this talk, I will discuss a closed-loop approach combining automated synthesis, photophysical characterization, and AI-guided prediction methods to identify organic light-harvesting molecules with optimized photostability. A Bayesian optimization framework is used to efficiently guide the search through a large molecular space using key physicochemical descriptors while maintaining a customizable tradeoff between exploitative and explorative sampling. Candidate molecules suggested by the AI framework are prepared via automated synthesis using a modular, “Lego-like” molecular building block approach based on Suzuki cross-coupling, followed by characterization of photophysical properties. Our results show that high-energy regions of the triplet state manifold are key to controlling molecular photostability in solution across a diverse chemical library of light-harvesting donor-bridge-acceptor oligomers. Remarkably, this insight emerged after automated modular synthesis and experimental characterization of only ~1.5% of the total chemical space of 2,200 oligomers. In the second part of the talk, I will discuss emerging directions including the extension of this framework to the design and development of organic electrochromic materials, light-harvesting vesicles for artificial photosynthesis, and new materials for molecular electronics. Overall, this work shows that interfacing physics-based modeling with closed-loop discovery campaigns – unimpeded by synthesis bottlenecks – can rapidly illuminate fundamental chemical insights and guide rational pursuit of frontier molecular functions.