Effect of Shear Flow and Precursor Polymer Design on Single-Chain Nanoparticle Formation

Single-chain nanoparticles (SCNPs) are a class of self-interacting polymers with proposed applications in fields ranging from catalysis to biomedical imaging. Because their morphology dictates suitability for specific applications, strategies to tailor structural outcomes are of significant interest. Here, we examine how SCNP morphology depends on both precursor chain attributes (e.g., linker fraction and backbone stiffness) and imposed shear flow in covalently bonded systems. Using coarse-grained molecular dynamics simulations, we generate an ensemble of structures from 10,800 unique SCNPs formed under either quiescent or shear conditions. We then apply unsupervised learning to construct a three-dimensional embedding space, enabling analysis of the relationships between precursor properties, flow conditions, and resulting morphologies. We further extend the analysis to reversibly bonded SCNPs to compare the relative importance of polymer properties and shear flow between covalently and reversibly bonded systems. These findings provide guidelines for designing SCNPs with targeted characteristics and demonstrate the utility of machine learning for analyzing their formation across diverse conditions.