Multiscale Modeling of Soft Matter
Predicting the physical properties of polymers and soft materials across multiple time and length scales remains one of the grand challenges in engineering and soft matter physics. To address this challenge, we have developed an Energy-Renormalization (ER) approach to coarse-graining polymers and soft materials. This method enables quantitative prediction of temperature-dependent behaviors by integrating principles from polymer physics, glass theories, and mechanics. The ER approach effectively resolves the critical issue of temperature transferability in multiscale modeling, representing a significant breakthrough for polymer simulations. These computational algorithms contribute to advancing the Materials Genome Initiative (MGI). (Sponsors: NIST, NSF)
Materials-by-Design for Functional Polymeric Materials
Nanostructured polymer materials, including coatings, thin films, and nanocomposites, are widely utilized in engineering and technology. At the nanoscale, surfaces and interfacial interactions with fillers and substrates critically impact their thermomechanical performance. Leveraging multiscale modeling techniques, our research reveals how chemically specific structures and intermolecular interactions govern the size-dependent behaviors of polymer thin films under nanoconfinement. By utilizing advanced computational methods, we have developed a materials-by-design framework capable of predicting key properties such as glass transition temperature, modulus, and toughness of polymer nanocomposites. (Sponsors: ONR, NSF, DOE)
Mechanics of Hierarchical Structures & Materials
The emergence of hierarchical and sheet-like materials offers great promise for advancing the functional performance of next-generation materials. Using multiscale modeling, we have uncovered that the overall crumpling behaviors of nanosheets are associated with edge-bending, self-adhesion, and compression mechanisms. Notably, our simulation predicts that the graphene melt exhibits fluid-like properties analogous to linear-chain polymers, having a high glass-transition temperature. For the first time, our theoretical framework establishes an analogy between graphene melt and polymers, enabling the extension of structure-property relationships for sheet materials. (Sponsors: NSF, ARO, NASA)
AI/ML Data-Driven Materials by Design
Artificial Intelligence (AI) and Machine Learning (ML) techniques are revolutionizing materials discovery and development. Our research employs AI/ML approaches for modeling and designing materials, focusing on predicting the properties and performance of complex conjugated polymers, functional coatings, and crosslinked networks. By integrating molecular modeling, theoretical insights, and ML, we have identified the critical roles of key molecular features in influencing the physical properties of complex polymers, aiding the development of structure-property relationships. The established predictive framework and ML model could be ready to use for the design of high-performance multifunctional materials. (Sponsors: ONR, DOE)
Acknowledging Our Sponsors!
