Executive Summary : | Multiscale modeling is a valuable tool for understanding the behavior of complex soft materials. Different numerical approaches, such as molecular dynamics and dissipative particle dynamics simulations, can be integrated to provide a comprehensive physical understanding of the behavior and functionality of these materials at the atomic/molecular level. The proposed research aims to enhance our understanding of soft matter physics. This is inspired by the structural order in natural and biological systems that involve integrating multiple components into a cohesive design capable of achieving complex and adaptable behavior. Multifunctional polymers and networks, such as gels, have unique properties and potential applications in a variety of fields, such as biomedicine, sensors, actuators, and drug delivery. Recent advancements in the field include the development of gels that can self-heal when damaged, allowing them to be used in applications where mechanical stability is critical. Ongoing research focuses on developing novel materials with improved properties and functions and exploring new applications in biomedicine and nanotechnology. Understanding the phase separation kinetics of multicomponent fluid mixtures of different topologies in d=3 systems is another direction of the proposed work. This phenomenon involves the spontaneous separation of various components of a mixture into distinct phases driven by the thermodynamic tendency to minimize the system's free energy. The current research efforts focus on developing more accurate and efficient simulation techniques, exploring new materials and mixtures, and applying the insights gained from these studies to design new materials with tailored properties. To regulate the evolved morphologies and network properties further, different factors such as hydrodynamic effects, chemical activity, pH changes and solvent quality, and long-range electrostatic interactions can be integrated into the simulation model. Overall, computational modeling could be a robust tool to capture the complexity of these systems. |