Multiscale Modeling of Soft Matter
The bottom-up prediction of the physical properties of polymers and soft materials at multiple time and length scales is one of the grand challenges in engineering and soft matter physics. To address this issue, we have recently established an Energy-Renormalization (ER) approach to coarse-graining polymers and soft materials and predicting their temperature-dependent behaviors by exploiting polymer physics, glass theories, and mechanics. This achievement has addressed one of the critical challenges (i.e., the temperature transferability issue) in the multiscale modeling of polymers. The established ER approach and computational algorithms are one of the essential contributions under the scope of the Materials Genome Initiative (MGI). (Sponsors: NIST, NSF)
Materials-by-Design for Functional Polymeric Materials
Nanostructured polymer materials (i.e., coatings, thin films, and nanocomposites) have been widely applied in engineering and technology. At a nanoscale, the surfaces and interfacial interactions with filler and substrate materials strongly influence their thermomechanical performances. Using multiscale modeling, our research has uncovered how the chemically specific structures and intermolecular interactions govern the size-dependent behaviors of polymer thin films under nanoconfinement. By applying advanced computational techniques, we have established a materials-by-design framework to predict the glass transition, 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, it is uncovered that the overall crumpling behaviors of nanosheets are associated with edge-bending, self-adhesion, and further compression mechanisms. Remarkably, our simulation predicts that the graphene melt exhibits fluid-like properties analogous to linear-chain polymers, having a high glass-transition temperature. Our results, for the first time, demonstrate an analogy between graphene melt and polymers through theoretical considerations, which is crucial to developing an 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) have successfully accelerated materials discovery and development in various applications. In our recent efforts, we have applied AI/ML techniques for material modeling and design, including property and performance prediction of complex conjugated polymers, functional coatings, and crosslinked networks. Moreover, molecular modeling and theory in conjunction with ML uncover 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)