CAREER: Machine Learned Coarse-grained Modeling for Mechanics of Thermoplastic Elastomers

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This Faculty Early Career Development (CAREER) grant will support fundamental research to understand complex mechanical behaviors of thermoplastic elastomers (TPEs). Biodegradable TPEs are promising recyclable and sustainable polymers with minimal environmental impact. They have the potential to be used as protective coatings for cell phones, artificial muscles for soft robotics, and polymer electrolytes for batteries. However, few of these applications have been effectively realized due to the limited understanding of TPEs' synthesis-structure-property relation. This research project aims to understand and quantify the link between synthesis, microstructure, and mechanical property of TPEs, with the help of multi-scale computational modeling, data science (machine learning), and experimental validation. With tailored mechanical properties, these biodegradable and environmentally friendly polymers can be widely used to enable an array of novel structural and device applications, alleviating the plastic pollution crisis. The project includes an education and outreach plan to train diverse groups of next-generation engineers through a variety of avenues: production of educational movies for the general public and K-12 students, engineering education for K-12 students through Pre-Engineering and Explore Engineering Programs, and providing research experience for undergraduate and graduate students, especially the underrepresented groups, through internships at industries and national labs. The research objective of this project is to formulate a machine-learned coarse-grained model for TPEs with thermodynamic consistency, temperature transferability, and representability. TPEs are segmented copolymers composed of hard segments and soft segments, forming a two-phase microstructure. Thus, the machine-learned coarse-grained model can be used to understand microphase separation and its contribution to mechanical behaviors of TPEs, leading to the well-defined synthesis-structure-property relation. Specifically, this project aims to: 1) establish the machine-learned coarse-grained model for TPEs through deep neural networks and an active learning scheme; 2) integrate coarse-grained molecular simulations and constitutive modeling to achieve a meaningful structure-property relation of TPEs; 3) explore a novel class of sequence-defined TPEs with tailored microstructures and mechanical properties. The fundamental understanding of the synthesis-structure-property relationship will highlight a clear design path for experimentalists to utilize biodegradable TPEs for a broad range of applications, e.g., sound and vibration damping materials, shape-memory materials, and adaptive solar control materials. This computational framework can be readily adapted and generalized to many other polymeric materials for understanding their structure-property relations, such as fatigue-resistant hydrogels and protein-mimetic polymers, with phase-separated microstructures.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.