Future materials-science research will involve autonomous synthesis and characterization, requiring an approach that combines machine learning, robotics, and big data. In this paper, we highlight our recent experiments in autonomous synthesis and resistance minimization of Nb-doped TiO2 thin films. Combining Bayesian optimization with robotics, these experiments illustrate how the required speed and volume of future big-data collection in materials science will be achieved and demonstrate the tremendous potential of this combined approach. We briefly discuss the outlook and significance of these results and advances.

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The first asymmetric epoxidation of isolated carbon–carbon double bonds by a chiral salen complex using ubiquitous Fe(III) as a center‐metal is described. By simultaneously introducing fluorous tags and tert‐butyl groups into the ligand of the salen complex, asymmetric epoxidation is achieved. The fluorous tags act as both the electron‐withdrawing groups, to improve the catalytic activity for oxidation, and the driving force to form a unique asymmetric stereo environment. Crystallographic analysis of the complex revealed that the catalyst has a distinctive umbrella structure based on intramolecular fluorophilic effect. This is the first example of asymmetric catalytic space construction that exploits fluorous space‐interaction of neighboring fluorous tags.