Collaborative Research: Designing Polymer Grafted-Nanoparticle Melts through a Hierarchical Computational Approach

Northwestern University, Evanston IL

Investigators

Abstract

The Division of Materials Research and the Division of Chemical, Bioengineering, Environmental and Transport Systems contribute funds to this award, which supports research to develop an accurate hierarchical modeling approach aimed at understanding the structural and mechanical properties of materials comprised only of inorganic nanoparticles chemically grafted with polymer chains. Polymer grafted nanoparticles represent a paradigm shift in how we fabricate, use, and recycle plastics because these composites have the potential to outperform traditional hybrids (i.e., physical mixtures of polymers and nanoparticles) in terms of strength and toughness. This is because the polymers are directly attached onto nanoparticles, thus ensuring that these materials constitute a chemically fixed mixture of polymers and nanoparticles – physical mixtures, on the other hand, tend to demix. However, variability in the size of the nanoparticles and their grafts, which is known as dispersity, results in unusual rheological and fracture properties that remain poorly understood. The proposed research will employ molecular simulations, theory, and machine learning to help explain how dispersity causes these properties and how it could be further exploited for discovering materials that surpass the current capabilities of nanocomposites. These research ideas are closely coupled to educational and outreach objectives to enrich the K-12 and undergraduate pipeline for STEM students. For this purpose, the team will create online learning modules for simulations, polymer physics, machine learning, and materials design. Other activities include interdisciplinary training of graduate students through the Predictive Science and Engineering Design Cluster at Northwestern University, sharing of force field and software developments, software commercialization, and new research opportunities for underrepresented student groups. The Division of Materials Research and the Division of Chemical, Bioengineering, Environmental and Transport Systems contribute funds to this award. The project addresses a critical knowledge gap in understanding how nanoscale interfacial chemistry, confinement and structural variations (grafting density, graft size and core size dispersities) in multi-component polymer-grafted nanoparticles (PGNs) govern dry layer extension and melt layer interdigitation, and consequently the emergent mesoscale ordering, dynamics, and failure mechanisms of PGNs. A barrier towards understanding the underlying molecular mechanisms is that existing computational paradigms, such as molecular simulations, either lack the fidelity or efficiency for predicting the structure, dynamics and hence properties of these systems. This issue will be addressed through a new scale-bridging method that will further coarse-grain PGNs to a nanoparticle level using chemistry-specific upscaling methods, and then use complementary data from finer models at two levels of hierarchy to inform Latent Variable Gaussian Process machine learning models. The adaptive machine learning model will enable rapid query of PGN properties in unexplored parametric spaces. The project encompasses three research thrusts: 1) Derivation of PGN pair potentials for multi-component systems; 2) Investigation of particle size and graft bidispersity effects on structure and rheology; 3) Development of an adaptive machine learning model for multi-objective design of PGNs. Ultimately, this novel framework will result in multicomponent PGNs that will break strength-toughness tradeoffs that hamper traditional nanocomposites. These research activities will also address open questions in materials informatics such as incorporation of categorical variables representing chemical groups in machine learning, finding optimal simulation batch sizes, and quantifying uncertainty due to noisy measurements. Discovering PGNs that exhibit mechanical property enhancements due to colloidal transitions is critical for them to become scalable, processible, and broadly useful as nanocomposites. To enrich the K-12 and undergraduate pipeline for STEM students, the team will create new activities including online learning modules for simulations, polymers, machine learning, and materials design. Other activities include the interdisciplinary training of graduate students through the Predictive Science and Engineering Design Cluster, sharing of force field and machine learning software developments, software commercialization, and new research opportunities for underrepresented groups. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.