Multiblock copolymers are envisioned as promising materials with enhanced properties and functionality compared with their diblock/triblock counterparts. However, the current approaches can construct multiblock copolymers with a limited number of blocks but tedious procedures. Here, we report a thioester‐relayed in‐chain cascade copolymerization strategy for the easy preparation of multiblock copolymers with on‐demand blocks, in which thioester groups with on‐demand numbers are built in the polymer backbone by controlled/living polymerizations. These thioester groups further serve as the in‐chain initiating centers to trigger the acyl group transfer ring‐opening polymerization of episulfides independently and concurrently to extend the polymer backbone into multiblock structures. The compositions, number of blocks, and block degree of polymerization can be easily regulated. This strategy can offer easy access to a library of multiblock copolymers with ≈100 blocks in only 2 to 4 steps.

Multiblock copolymers are envisioned as promising materials with enhanced properties and functionality compared with their diblock/triblock counterparts. However, the current approaches can construct multiblock copolymers with a limited number of blocks but tedious procedures. Here, we report a thioester‐relayed in‐chain cascade copolymerization strategy for the easy preparation of multiblock copolymers with on‐demand blocks, in which thioester groups with on‐demand numbers are built in the polymer backbone by controlled/living polymerizations. These thioester groups further serve as the in‐chain initiating centers to trigger the acyl group transfer ring‐opening polymerization of episulfides independently and concurrently to extend the polymer backbone into multiblock structures. The compositions, number of blocks, and block degree of polymerization can be easily regulated. This strategy can offer easy access to a library of multiblock copolymers with ≈100 blocks in only 2 to 4 steps.

Inverse design of artificial chiral materials and nanostructures with strong and tunable chiroptical activities are highly desirable, owing to their potential applications in chiral sensing, enantioselective catalysis and chiroptical devices. In this study, we use an artificial intelligence (AI) guided robotic chemist to accurately predict chiroptical activities from the experimental absorption spectra and structure/process parameters, and generate chiral films with targeted chiroptical activities across the full visible spectrum. The robotic AI-chemist carries out entire process of chiral film constructing, characterizing, and testing. A machine learned reverse design model using spectrum embedded descriptors was developed to predict optimal structure/process parameters for any targeted chiroptical properties. A series of chiral films with high chiroptical activity (gabs~1.9) had been identified out of more than 100 million possible structures, and their feasible application in circular polarization-selective color filters for multiplex laser display and switchable CP luminescence had been demonstrated.