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

这项教师早期职业发展（Career）资助将支持基础研究，以了解热塑性弹性体（TPEs）的复杂力学行为。可生物降解的tpe是有前途的可回收和可持续的聚合物，对环境的影响最小。它们有潜力被用作手机的保护涂层、软体机器人的人造肌肉和电池的聚合物电解质。然而，由于对tpe的合成-结构-性能关系的认识有限，这些应用很少得到有效实现。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，了解和量化tpe的合成、微观结构和力学性能之间的联系。由于具有定制的机械性能，这些可生物降解和环保的聚合物可以广泛用于实现一系列新型结构和设备应用，缓解塑料污染危机。该项目包括一个教育和推广计划，通过多种途径培养不同群体的下一代工程师：为普通大众和K-12学生制作教育电影，通过工程预科和探索工程项目为K-12学生提供工程教育，并通过在工业和国家实验室实习，为本科生和研究生，特别是代表性不足的群体提供研究经验。本项目的研究目标是建立具有热力学一致性、温度可转移性和可表征性的tpe机器学习粗粒度模型。tpe是由硬段和软段组成的分段共聚物，形成两相微观结构。因此，机器学习的粗粒度模型可用于理解微相分离及其对tpe力学行为的贡献，从而形成定义良好的合成-结构-性能关系。具体而言，本项目旨在：1)通过深度神经网络和主动学习方案建立tpe的机器学习粗粒度模型；2)结合粗粒度分子模拟和本构建模，实现有意义的tpe结构-性能关系；3)探索一类具有定制显微结构和力学性能的序列定义的新型tpe。对合成-结构-性能关系的基本理解将为实验者提供一条清晰的设计路径，以利用可生物降解的TPEs进行广泛的应用，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。这种计算框架可以很容易地适应并推广到许多其他聚合物材料，以理解它们的结构-性能关系，例如具有相分离微结构的抗疲劳水凝胶和模拟蛋白质聚合物。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.