Artificial intelligence-guided closed-loop experimentation has emerged as a promising method for optimization of objective functions^1,2, 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.

人工智能引导的闭环实验已成为一种有前景的目标函数优化方法^1,2 ，但这种传统黑盒方法在发现新化学知识方面的巨大潜力尚未得到充分开发。在这里，我们报告了闭环实验与基于物理的特征选择和监督学习的集成，称为闭环转移（CLT），以在优化目标函数的同时产生化学见解。 CLT 用于检查各种有机电子应用中使用的光捕获供体-受体分子溶液中决定光稳定性的因素，并显示了基本见解，包括三重态流形高能区域的重要性。通过自动模块化合成和仅约 1.5% 的理论化学空间的实验表征，这是可能的。这种基于物理的光稳定性模型通过多个实验测试集得到加强，并通过调整溶剂的三重激发态能量进行验证，以突破闭环光稳定性优化过程中观察到的平台。 CLT 在其他材料系统中的进一步应用支持了该策略增强闭环策略的普遍性。总的来说，这些发现表明，将可解释的监督学习模型和基于物理的特征与闭环发现过程相结合可以快速提供基本的化学见解。