Closed-loop transfer enables artificial intelligence to yield chemical knowledge.

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

A Hydrogel Vitreous Substitute that Releases Antioxidant.

Current experimental vitreous substitutes only replace the physical functions of the natural vitreous humor. Removal of the native vitreous disrupts oxygen homeostasis in the eye, causing oxidative damage to the lens that likely results in cataract formation. Neither current clinical treatments nor other experimental vitreous substitutes consider the problem of oxidative stress after vitrectomy.