Sequence Patterning, Topology, and Morphology in Single-chain Nanoparticles: Insights from Graph Theory and Machine Learning

Single-chain nanoparticles are intriguing materials inspired by proteins that consist of a single precursor polymer chain that has collapsed into a stable structure. In many prospective applications, such as catalysis, the utility of a single-chain nanoparticle will intricately depend on the reliable formation of a mostly specific structure or morphology. However, it is not generally well understood how to reliably control the morphology of single-chain nanoparticles. To address this knowledge gap, we simulate the formation of 7,680 distinct single-chain nanoparticles from precursor chains that span a wide range of, in principle, tunable patterning characteristics of cross-linking moieties. Using a combination of graph-theoretic and machine learning analyses, we show how the overall fraction of functionalization and blockiness of cross-linking moieties biases the formation of specific topological descriptors and how such descriptors further promote certain local and global morphological characteristics. Importantly, using the total volume of simulation data, we illustrate and quantify the dispersity of morphologies that arise due to both the stochastic nature of collapse from a well-defined sequence as well as from the ensemble of sequences that correspond to a given specification of precursor parameters. Overall, this work critically assesses how precursor chains might be feasibly tailored to achieve given SCNP morphologies and a provides platform to pursue future sequence-based design.