Sequence Patterning, Morphology, and Dispersity in Single-Chain Nanoparticles: Insights from Simulation and Machine Learning

Roshan A. Patel, Sophia Colmenares, Michael A. Webb

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Single-chain nanoparticles (SCNPs) 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 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 7680 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 molecular simulation and machine learning analyses, we show how the overall fraction of functionalization and blockiness of cross-linking moieties biases the formation of certain local and global morphological characteristics. Importantly, we illustrate and quantify the dispersity of morphologies that arise due to 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. Moreover, we also examine the efficacy of precise sequence control in achieving morphological outcomes in different regimes of precursor parameters. Overall, this work critically assesses how precursor chains might be feasibly tailored to achieve given SCNP morphologies and provides a platform to pursue future sequence-based design.

Original languageEnglish (US)
Pages (from-to)284-294
Number of pages11
JournalACS Polymers Au
Issue number3
StatePublished - Jun 14 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Chemical Engineering (miscellaneous)
  • Chemistry (miscellaneous)
  • Materials Chemistry
  • Polymers and Plastics
  • Physical and Theoretical Chemistry
  • Organic Chemistry


  • dimensionality reduction
  • enzyme mimics
  • sequence effects
  • structure control
  • structure−property relationships
  • topology
  • unsupervised learning


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