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Grafting polymer chains to the surface of nanoparticles overcomes the challenge of nanoparticle dispersion within nanocomposites and establishes high-volume fractions that are found to enable enhanced material mechanical properties. This study utilizes coarse-grained molecular dynamics simulations to quantify how the shear modulus of polymer-grafted nanoparticle (PGN) systems in their glassy state depends on parameters such as strain rate, nanoparticle size, grafting density, and chain length. The results are interpreted through further analysis of the dynamics of chain conformations and volume fraction arguments. The volume fraction of nanoparticles is found to be the most influential variable in deciding the shear modulus of PGN systems. A simple rule of mixture is utilized to express the monotonic dependence of shear modulus on the volume fraction of nanoparticles. Due to the reinforcing effect of nanoparticles, shortening the grafted chains results in a higher shear modulus in PGNs, which is not seen in linear systems. These results offer timely insight into calibrating molecular design parameters for achieving the desired mechanical properties in PGNs.
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This content will become publicly available on October 14, 2026
A Generalized Machine-Learning Framework for Developing Alchemical Many-Body Interaction Models for Polymer-Grafted Nanoparticles
Polymer-grafted nanoparticles (PGNs) serve as highly customizable building blocks for technologically relevant self-assembled nanomaterials. Physics informed Inverse design strategies are crucial for expediting exploration of the massive associated design space by identifying optimal PGN attributes such as polymer length and grafting density to meet a target self-assembled structure. However, their success hinges on accurate description of how PGN interactions vary as a function of both particle positions and physical attributes. We introduce a framework to meet this need. Specifically, we develop an “alchemical” ML-IAM that describes how PGN interactions vary as a function of both inter-PGN distances and tunable PGN attributes, simultaneously. This model is an extension of the physics-informed and explicitly many-bodied ChIMES model. The resulting extended ChIMES (X-ChIMES) model is trained on PMF data for PGNs with varied polymer ligand length. We enable efficient training data generation by combining the forward-reverse steered molecular dynamics enhanced sampling approach with a grid-sampling scheme in the HOOMD-blue software package. We demonstrate the efficacy of ChIMES for generating CG models for PGNs with fixed design attributes, the development of the X-ChIMES model, and the application of the X-ChIMES model that enhances digital alchemy inverse-design simulations using HOOMD-blue and the ChIMES Calculator. This is the first application of ChIMES in modeling CG systems and coupling with an inverse design method to target nanomaterial self-assembly.
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- Award ID(s):
- 2302470
- PAR ID:
- 10678929
- Publisher / Repository:
- American Chemical Society
- Date Published:
- Journal Name:
- Journal of Chemical Theory and Computation
- Volume:
- 21
- Issue:
- 19
- ISSN:
- 1549-9618
- Page Range / eLocation ID:
- 9853 to 9867
- Format(s):
- Medium: X
- Associated Dataset(s):
- View Associated Dataset(s) >>
- Sponsoring Org:
- National Science Foundation
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