Targeted sequence design within the coarse-grained polymer genome

Michael A. Webb, Nicholas E. Jackson, Phwey S. Gil, Juan J. de Pablo

Research output: Contribution to journalArticlepeer-review

Abstract

The chemical design of polymers with target structural and/or functional properties represents a grand challenge in materials science. While data-driven design approaches are promising, success with polymers has been limited, largely due to limitations in data availability. Here, we demonstrate the targeted sequence design of single-chain structure in polymers by combining coarse-grained modeling, machine learning, and model optimization. Nearly 2000 unique coarse-grained polymers are simulated to construct and analyze machine learning models. We find that deep neural networks inexpensively and reliably predict structural properties with limited sequence information as input. By coupling trained ML models with sequential model-based optimization, polymer sequences are proposed to exhibit globular, swollen, or rod-like behaviors, which are verified by explicit simulations. This work highlights the promising integration of coarse-grained modeling with data-driven design and represents a necessary and crucial step toward more complex polymer design efforts.

Original languageEnglish (US)
Article numberabc6216
JournalScience Advances
Volume6
Issue number43
DOIs
StatePublished - Oct 21 2020

All Science Journal Classification (ASJC) codes

  • General

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