Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

The transition toward sustainable polymer materials would be helped by the replacement of petroleum‐based monomers with bio‐based alternatives. This work presents an integrated machine learning framework that aims at accelerating this transition by simultaneously predicting four critical properties of bio‐based monomers to be used in emulsion polymerization processes: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. Improved models for each property were developed using neural networks and gradient boosting approaches, demonstrating enhanced predictive accuracy compared to existing methods. These models were integrated into a unified selection tool that systematically evaluates candidate bio‐based monomer pairs as replacements for conventional formulations, while maintaining target copolymer properties. The framework was validated through two experimental case studies: replacement of n‐butyl acrylate/methyl methacrylate for coating applications, and replacement of n‐butyl acrylate/styrene for adhesive applications. Using the selected bio‐based candidates, both bio‐based systems achieved high conversions and demonstrated glass transition temperatures comparable to their conventional counterparts, confirming the framework's capability to identify viable bio‐based alternatives to conventional petrochemical based polymeric systems.