<scp>AI</scp> ‐Guided Design of Polymer‐Supported Metal Catalysts: From Predictive Machine Learning to Bayesian Optimization

ABSTRACT Polymer immobilized metal catalysts present a platform for sustainable chemistry, but their design is often hindered by a vast, mixed‐variable design space consisting of polymer architecture, functional chemistry, and metal loading. Conventional iterative testing is inefficient for these systems. This perspective evaluates the role of artificial intelligence in accelerating the design and optimization of polymer‐supported metal catalysts, with emphasis on the transition from predictive machine learning to Bayesian optimization. Predictive modeling approaches relevant to polymer‐based materials are outlined, followed by a potential workflow and discussion of common pitfalls. Physics‐informed neural networks and related hybrid approaches are also considered as complementary alternatives when mechanistic knowledge, transport models, or thermodynamic constraints are available. We further highlight pioneering case studies that demonstrate the practical relevance of Bayesian optimization for polymer‐supported catalytic systems. We argue that the most promising near‐term route is an integrated hybrid workflow that combines curated experimental and computational datasets with interpretable, physics‐aware surrogate models and uncertainty‐guided Bayesian optimization to efficiently determine the next formulations or operating conditions for discovery.