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

03 March 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

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.

Keywords

graph theory
assembly
QSPR
topology
unsupervised learning

Supplementary materials

Title
Description
Actions
Title
Supporting information document + data
Description
PDF of supporting information text relevant data for single-chain nanoparticles
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.