Polymeric materials have a broad range of mechanical and physical properties.They have been widely used in material science,biomedical engineering,chemical engineering,and mechanical engineering.The introduction of ac...Polymeric materials have a broad range of mechanical and physical properties.They have been widely used in material science,biomedical engineering,chemical engineering,and mechanical engineering.The introduction of active elements into the soft matrix of polymers has enabled much more diversified functionalities of polymeric materials,such as self-healing,electroactive,magnetosensitive,pH-responsive,and many others.To further enable applications of these multifunctional polymers,a mechanistic modeling method is required and of great significance,as it can provide links between materials’micro/nano-structures and their macroscopic mechanical behaviors.Towards this goal,molecular simulation plays an important role in understanding the deformation and evolution of polymer networks under external loads and stimuli.These molecular insights provide physical guidance in the formulation of mechanistic-based continuum models for multifunctional polymers.In this perspective,we present a molecular simulation-guided and physics-informed modeling framework for polymeric materials.Firstly,the physical theory for polymer chains and their networks is briefly introduced.It serves as the foundation for mechanistic-models of polymers,linking their chemistry,physics,and mechanics together.Secondly,the deformation of the polymer network is used to derive the strain energy density functions.Thus,the corresponding continuum models can capture the intrinsic deformation mechanisms of polymer networks.We then highlight several representative examples across multiphysics coupling problems to describe in detail for this proposed framework.Last but not least,we discuss potential challenges and opportunities in the modeling of multifunctional polymers for future research directions.展开更多
基金the support from the Interdisciplinary Multi-Investigator Materials Proposals(IMMP)program of the Institute of Materials Science at the University of Connecticutfunding support from the National Science Foundation(CMMI-1762661 and CMMI-1934829)the funding support from the National Science Foundation(CMMI-1762567 and CMMI-1943598).
文摘Polymeric materials have a broad range of mechanical and physical properties.They have been widely used in material science,biomedical engineering,chemical engineering,and mechanical engineering.The introduction of active elements into the soft matrix of polymers has enabled much more diversified functionalities of polymeric materials,such as self-healing,electroactive,magnetosensitive,pH-responsive,and many others.To further enable applications of these multifunctional polymers,a mechanistic modeling method is required and of great significance,as it can provide links between materials’micro/nano-structures and their macroscopic mechanical behaviors.Towards this goal,molecular simulation plays an important role in understanding the deformation and evolution of polymer networks under external loads and stimuli.These molecular insights provide physical guidance in the formulation of mechanistic-based continuum models for multifunctional polymers.In this perspective,we present a molecular simulation-guided and physics-informed modeling framework for polymeric materials.Firstly,the physical theory for polymer chains and their networks is briefly introduced.It serves as the foundation for mechanistic-models of polymers,linking their chemistry,physics,and mechanics together.Secondly,the deformation of the polymer network is used to derive the strain energy density functions.Thus,the corresponding continuum models can capture the intrinsic deformation mechanisms of polymer networks.We then highlight several representative examples across multiphysics coupling problems to describe in detail for this proposed framework.Last but not least,we discuss potential challenges and opportunities in the modeling of multifunctional polymers for future research directions.