AI-guided closed-loop experimentation has recently emerged as a promising method to optimize objective functions in materials discovery. However, achieving the full potential of this approach in the chemical sciences requires new methods to efficiently access large chemical spaces. In this talk, Professor Schroeder will discuss a closed-loop approach combining automated synthesis, materials 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.
His 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 synthesis and experimental characterization of only ~1.5% of the total chemical space of 2,200 oligomers. In the second part of the talk, Professor Schroeder will discuss emerging directions including the extension of this framework to the design and development of function-encoded molecular building blocks to enhance photostability and the discovery of new organic electrochromic materials. 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.
About Professor Charles Schroeder
Charles Schroeder is the James Economy Professor in the Department of Materials Science and Engineering and the Department of Chemical & Biomolecular Engineering at the University of Illinois at Urbana-Champaign. At Illinois, Professor Schroeder leads the AI for Materials (AIM) Group in the Beckman Institute for Advanced Science and Technology and holds affiliate status in the Department of Chemistry, Department of Bioengineering, Center for Biophysics and Quantitative Biology, the Carl R. Woese Institute for Genomic Biology, and the Materials Research Lab (MRL). He received his BS in Chemical Engineering from Carnegie Mellon University in 1999, followed by an MS in 2001 and PhD in 2005 in Chemical Engineering from Stanford University under the supervision of Professor Eric Shaqfeh and Professor Steven Chu. Prior to joining Illinois in 2008, he was a postdoctoral fellow in the Department of Chemistry and Chemical Biology at Harvard University. Professor Schroeder has received several awards, including a Packard Fellowship for Science and Engineering, a Camille Dreyfus Teacher-Scholar Award, an NSF CAREER Award, the Arthur B. Metzner Award from the Society of Rheology, the Dean’s Award for Excellence in Research at Illinois, and an NIH Pathway to Independence Award (K99/R00). Professor Schroeder is a Fellow of the American Association for the Advancement of Science (AAAS), the Society of Rheology (SOR), and the American Physical Society (APS).
