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Abstract Sustainable polymers from renewable resources have been gaining importance due to their recyclability and reduced environmental impact. However, their development through conventional trial‐and‐error methods remains inefficient and resource‐intensive. Machine learning (ML) has emerged as a powerful tool in polymer science, enabling rapid prediction, and discovery of new chemicals and materials. In this review, we examine emerging trends in ML applications for sustainable polymer development, focusing on catalyst discovery, property optimization, and new polymer design. We analyze unique challenges in applying ML to sustainable polymers and evaluate proposed solutions, providing insights for future development in this rapidly evolving field. 

Abstract Linear poly(α‐hydroxy acids) are important degradable polymers, and they can be efficiently prepared by ring‐opening polymerization of O‐carboxyanhydrides with pendant functional groups. However, attempts to prepare cyclic poly(α‐hydroxy acids) have been plagued by side reactions, including epimerization and uncontrolled intramolecular chain transfers or termination, that prevent the synthesis of high‐molecular‐weight stereoregular cyclic polyesters. Herein, we report a scalable method for the synthesis of high‐molecular‐weight (>100 kDa) stereoregular functionalized cyclic poly(α‐hydroxy acids) by means of controlled polymerization of O‐carboxyanhydrides using a catalytic system consisting of a lanthanum complex with a sterically bulky ligand and a manganese silylamide. Additionally, using this system, we could readily prepare cyclic block poly(α‐hydroxy acids) by means of sequential addition of O‐carboxyanhydrides. The obtained cyclic polyesters and their cyclic block copolyesters exhibit distinctive physicochemical properties—including elevated phase transition temperature, improved toughness, and reduced viscosity—compared to their linear counterparts. 

Abstract 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 (%V_bur) and the highest occupied molecular orbital energy (E_HOMO), that can access quantitative and predictive models for catalyst development. 

Abstract 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.