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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.