Conducting polymerizations under continuous-flow conditions affords distinct advantages over batch experimentation and has increasingly been employed by the research community for chain-growth polymerizations to accelerate material discovery and to finely tune material properties. This work now expands on the reported advances by demonstrating the utility of continuous flow for polyaddition reactions of polyurethanes (PUs). Various reactor configurations enable the on- demand organocatalytic one-step synthesis of linear PUs with tailored soft-to-hard segment ratios in 3-5 min at room temperature. In-line analytics is implemented for real-time process monitoring and to develop process control mechanisms, increasing the control over monomer conversion and material quality and minimizing batch-to-batch variations. The reported strategies thus provide a guideline for the straightforward preparation of libraries of commercially relevant PU materials.

Mechanical metamaterials are usually designed to show desired responses to prescribed forces. In some applications, the desired force-response relationship is hard to specify exactly, but examples of forces and desired responses are easily available. Here, we propose a framework for supervised learning in thin, creased sheets that learn the desired force-response behavior by physically experiencing training examples and then, crucially, respond correctly (generalize) to previously unseen test forces. During training, we fold the sheet using training forces, prompting local crease stiffnesses to change in proportion to their experienced strain. We find that this learning process reshapes nonlinearities inherent in folding a sheet so as to show the correct response for previously unseen test forces. We show the relationship between training error, test error, and sheet size (model complexity) in learning sheets and compare them to counterparts in machine-learning algorithms. Our framework shows how the rugged energy landscape of disordered mechanical materials can be sculpted to show desired force-response behaviors by a local physical learning process.