Many-body potential for simulating the self-assembly of polymer-grafted nanoparticles in a polymer matrix
Year: 2023
Type: article
Abstract: Many-body interactions between polymer-grafted nanoparticles (NPs) play a key role in promoting their assembly into low-dimensional structures within polymer melts, even when the particles are spherical and isotropically grafted. However, capturing such interactions in simulations of NP assembly is very challenging because explicit modeling of the polymer grafts and melt chains is highly computationally expensive, even using coarse-grained models. Here, we develop a many-body potential for describing the effective interactions between spherical polymer-grafted NPs in a polymer matrix through a machine-learning approach. The approach involves using permutationally invariant polynomials to fit two- and three-body interactions derived from the potential of mean force calculations. The potential developed here reduces the computational cost by several orders of magnitude, thereby, allowing us to explore assembly behavior over large length and time scales. We show that the potential not only reproduces previously known assembled phases such as 1D strings and 2D hexagonal sheets, which generally cannot be achieved using isotropic two-body potentials, but can also help discover interesting phases such as networks, clusters, and gels. We demonstrate how each of these assembly morphologies intrinsically arises from a competition between two- and three-body interactions. Our approach for deriving many-body effective potentials can be readily extended to other colloidal systems, enabling researchers to make accurate predictions of their behavior and dissect the role of individual interaction energy terms of the overall potential in the observed behavior.
Source: npj Computational Materials
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,190
Funders: National Science Foundation, U.S. Department of Energy, University of California, San Diego, Materials Research Science and Engineering Center, Harvard University, Division of Materials Research, Office of Advanced Cyberinfrastructure, Materials Research Science and Engineering Center, University of California, San Diego, Lawrence Livermore National Laboratory
Awards: G1088636562, G1186292489, G1356433355, G1453181110, G14554683, G1554045874, Track 4: Advanced CI Coordination Ecosystem: Monitoring and Measurement Services, G2145270357, G3406661761, G3562386289, Track 1: ACCESS Resource Allocations Marketplace and Platform Services (RAMPS), G4010157413, ACO: An Open CI Ecosystem to Advance Scientific Discovery (OpenCI), Track 3: COre National Ecosystem for CyberinfrasTructure (CONECT), G5212984984, G5282220012, G5367127336, G5464919783, G6121277264, UC San Diego MRSEC: an NSF Materials Research Science and Engineering Center, XSEDE: eXtreme Science and Engineering Discovery Environment, G6965887028, G8174472116, Track 2: Customized Multi-tier Assistance, Training, and Computational Help (MATCH) for End User ACCESS to CI, G8412107296, G8692219177, G991281326
OpenAlex ID: W4389784588