Closed-Loop Transfer Enables AI to Yield Chemical Knowledge

Abstract

AI-guided closed-loop experimentation has recently emerged as a promising method to optimize objective functions,1,2 but the substantial potential of this traditionally black-box approach to reveal new scientific knowledge has remained largely untapped. Here, we report a new AI-guided approach, dubbed Closed-Loop Transfer (CLT), that integrates closed-loop experiments with physics-based feature selection and supervised learning to yield new scientific knowledge in parallel with optimization of objective functions. CLT surprisingly revealed that high-energy regions of the triplet state manifold are paramount in dictating 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 theoretical chemical space. Supervised learning models considering millions of combinations of 100+ physics-based descriptors further showed that high energy triplet states most strongly correlate with photostability, while excluding more commonly considered predictors such as the lowest energy triplet state. The physics-informed model for photostability was even further confirmed and then strengthened using an explicit experimental test set, validating the substantial power of the CLT method. Broadly, these findings show that interfacing physics-based modeling with closed-loop discovery campaigns unimpeded by synthesis bottlenecks can rapidly illuminate fundamental chemical insights and guide more rational pursuit of frontier molecular functions.

Self-Driving Laboratories for Chemistry and Materials Science

“…The work of Angello et al demonstrated an SDL focused on discovering organic optoelectronics with good photostability, particularly for organic photovoltaic (OPV) applications. 643 Like in the previously discussed works on automated organic molecule synthesis, 610,638 the chemical space was predefined through the combination of molecular fragments through iterative Suzuki-Miyaura coupling reactions. 428 In this case, the fragments were acceptor and donor complexes connected by a bridge fragment; this is a common design for OPV applications, with light-induced charge separation encouraged by the difference in local electronic energy levels.…”