Closed-loop transfer enables artificial intelligence to yield chemical knowledge

• 成果类型: Article
• 署名作者: Angello, Nicholas H.; Friday, David M.; Hwang, Changhyun; Yi, Seungjoo; Cheng, Austin H.; Torres-Flores, Tiara C.; Jira, Edward R.; Wang, Wesley; Aspuru-Guzik, Alan; Burke, Martin D.; Schroeder, Charles M.; Diao, Ying; Jackson, Nicholas E.
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; University of Toronto; Vector Institute for Artificial Intelligence; University of Toronto; Canadian Institute for Advanced Research (CIFAR); University of Toronto; University of Toronto; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign
• 刊物名称: Nature
• ISSN/ISSBN: 0028-5563
• DOI: 10.1038/s41586-024-07892-1
• 发表日期: 2024-09-12
• 页码: 351-358
• 关键词: light; photodegradation; photoprotection; mechanism; polymers

摘要

Artificial intelligence-guided closed-loop experimentation has emerged as a promising method for optimization of objective functions1,2, but the substantial potential of this traditionally black-box approach to uncovering new chemical knowledge has remained largely untapped. Here we report the integration of closed-loop experiments with physics-based feature selection and supervised learning, denoted as closed-loop transfer (CLT), to yield chemical insights in parallel with optimization of objective functions. CLT was used to examine the factors dictating the photostability in solution of light-harvesting donor-acceptor molecules used in a variety of organic electronics applications, and showed fundamental insights including the importance of high-energy regions of the triplet state manifold. This was possible following automated modular synthesis and experimental characterization of only around 1.5% of the theoretical chemical space. This physics-informed model for photostability was strengthened using multiple experimental test sets and validated by tuning the triplet excited-state energy of the solvent to break out of the observed plateau in the closed-loop photostability optimization process. Further applications of CLT to additional materials systems support the generalizability of this strategy for augmenting closed-loop strategies. Broadly, these findings show that combining interpretable supervised learning models and physics-based features with closed-loop discovery processes can rapidly provide fundamental chemical insights. Integration of closed-loop experiments with physics-based feature selection and supervised learning, denoted as closed-loop transfer, yields chemical insights in parallel with optimization of objective functions.