Closed-loop transfer enables artificial intelligence to yield chemical knowledge

Nicholas H. Angello, David M. Friday, Changhyun Hwang, Seungjoo Yi, Austin H. Cheng, Tiara C. Torres-Flores, Edward R. Jira, Wesley Wang, Alán Aspuru-Guzik (), Martin D. Burke (), Charles M. Schroeder (), Ying Diao () and Nicholas E. Jackson ()
Additional contact information
Nicholas H. Angello: University of Illinois at Urbana-Champaign
David M. Friday: University of Illinois at Urbana-Champaign
Changhyun Hwang: University of Illinois at Urbana-Champaign
Seungjoo Yi: University of Illinois at Urbana-Champaign
Austin H. Cheng: University of Toronto
Tiara C. Torres-Flores: University of Illinois at Urbana-Champaign
Edward R. Jira: University of Illinois at Urbana-Champaign
Wesley Wang: University of Illinois at Urbana-Champaign
Alán Aspuru-Guzik: University of Toronto
Martin D. Burke: University of Illinois at Urbana-Champaign
Charles M. Schroeder: University of Illinois at Urbana-Champaign
Ying Diao: University of Illinois at Urbana-Champaign
Nicholas E. Jackson: University of Illinois at Urbana-Champaign

Nature, 2024, vol. 633, issue 8029, 351-358

Abstract: Abstract 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.

Date: 2024
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DOI: 10.1038/s41586-024-07892-1

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