Five categories of valuable N-containing compounds can be selectively synthesized by the catalytic tandem reduction of nitroarenes with in situ generated Pd nanoclusters as the catalyst.

Transforming renewable resources into functional and degradable polymers is driven by the ever‐increasing demand to replace unsustainable polyolefins. However, the utility of many degradable homopolymers remains limited due to their inferior properties compared to commodity polyolefins. Therefore, the synthesis of sequence‐defined copolymers from one‐pot monomer mixtures is not only conceptually appealing in chemistry, but also economically attractive by maximizing materials usage and improving polymers’ performances. Among many polymerization strategies, ring‐opening (co)polymerization of cyclic monomers enables efficient access to degradable polymers with high control on molecular weights and molecular weight distributions. Herein, we highlight recent advances in achieving one‐pot, sequence‐controlled polymerizations of cyclic monomer mixtures using a single catalytic system that combines multiple catalytic cycles. The scopes of cyclic monomers, catalysts, and polymerization mechanisms are presented for this type of sequence‐controlled ring‐opening copolymerization.

Stereoselective ring-opening polymerization catalysts are used to produce degradable stereoregular poly(lactic acids) with thermal and mechanical properties that are superior to those of atactic polymers. However, the process of discovering highly stereoselective catalysts is still largely empirical. We aim to develop an integrated computational and experimental framework for efficient, predictive catalyst selection and optimization. As a proof of principle, we have developed a Bayesian optimization workflow on a subset of literature results for stereoselective lactide ring-opening polymerization, and using the algorithm, we identify multiple new Al complexes that catalyze either isoselective or heteroselective polymerization. In addition, feature attribution analysis uncovers mechanistically meaningful ligand descriptors, such as percent buried volume (%Vbur) and the highest occupied molecular orbital energy (EHOMO), that can access quantitative and predictive models for catalyst development.

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