Machine learning (ML), material genome, and big data approaches are highly overlapped in their strategies, algorithms, and models. They can target various definitions, distributions, and correlations of concerned physical parameters in given polymer systems, and have expanding applications as a new paradigm indispensable to conventional ones. Their inherent advantages in building quantitative multivariate correlations have largely enhanced the capability of scientific understanding and discoveries, thus facilitating mechanism exploration, target prediction, high‐throughput screening, optimization, and rational and inverse designs. This article summarizes representative progress in the recent two decades focusing on the design, preparation, application, and sustainable development of polymer materials based on the exploration of key physical parameters in the composition–process–structure–property–performance relationship. The integration of both data‐driven and physical insights through ML approaches to deepen fundamental understanding and discover novel polymer materials is categorically presented. Despite the construction and application of robust ML models, strategies and algorithms to deal with variant tasks in polymer science are still in rapid growth. The challenges and prospects are then presented. We believe that the innovation in polymer materials will thrive along the development of ML approaches, from efficient design to sustainable applications.

Recently developed reversibly interlocked polymer networks are made from two pre-formed crosslinked polymers containing reversible covalent bonds via topological rearrangement. Unlike interpenetrating polymer networks, the resultant is rather homogeneous regardless of the miscibility between the parent single networks, and can be repeatedly self-healed and unlocked/re-locked. The present work focuses on revealing the critical role of the hydrogen bonds between the single networks in the construction of the interlocked networks, cooperating with dissociation and re-association of the built-in reversible covalent bonds. Based on the systematic investigations of the small molecules, single networks and interlocked networks, the inter-component hydrogen bonds of the interlocked networks are found to be able to be built-up, which are more stable than the intra-molecular hydrogen bonds of the single networks. They help to pull the chains from different single networks together, preventing the occurrence of phase separation during interlocking and implementing the valuable forced miscibility. Besides, abundant but weak inter-component hydrogen bonds are preferred. The outcomes not only show the hitherto unknown part of the mechanism involved in the formation of the interlocked networks, but also benefit the design of new members of interlocked networks.