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

这笔学院早期职业发展(Career)补助金将支持基础研究，以了解热塑性弹性体(TPE)的复杂力学行为。可生物降解的TPE是一种对环境影响最小的可回收和可持续发展的聚合物。它们有可能被用作手机的保护层，软机器人的人造肌肉，以及电池的聚合物电解液。然而，由于对热塑性弹性体的合成-结构-性能关系的认识有限，这些应用还没有得到有效的实现。本研究项目旨在通过多尺度计算建模、数据科学(机器学习)和实验验证来理解和量化热塑性弹性体的合成、微观结构和力学性能之间的联系。这些可生物降解的环保聚合物具有量身定制的机械性能，可广泛用于一系列新颖的结构和设备应用，缓解塑料污染危机。该项目包括一项教育和推广计划，旨在通过各种途径培训不同的下一代工程师群体：为普通公众和K-12学生制作教育电影；通过工程预科课程和探索工程课程为K-12学生提供工程教育；通过在行业和国家实验室的实习，为本科生和研究生提供研究经验，尤其是代表不足的群体。本项目的研究目标是建立一个具有热力学一致性、温度传递性和代表性的TPES的机器学习粗粒度模型。热塑性弹性体是由硬段和软段组成的嵌段共聚物，形成两相微结构。因此，机器学习的粗晶模型可以用来理解微相分离及其对热塑性弹性体力学行为的贡献，从而得到明确的合成-结构-性能关系。具体地说，本项目的目标是：1)通过深度神经网络和主动学习方案建立热塑性弹性体的机器学习粗粒度模型；2)将粗粒度分子模拟和本构模型相结合，以获得有意义的热塑性弹性体的结构-性能关系；3)探索一类具有定制的微观结构和力学性能的新型序列定义的热塑性弹性体。对合成-结构-性能关系的基本了解将为实验者利用可生物降解的热塑性弹性体在广泛的应用中提供明确的设计途径，例如，声音和振动阻尼材料、形状记忆材料和自适应太阳能控制材料。这一计算框架可以很容易地适应和推广到许多其他聚合物材料，以了解它们的结构-性能关系，如耐疲劳水凝胶和具有相分离微结构的蛋白质模拟聚合物。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.