Developing and Understanding Thermally Conductive Polymers by Combining Molecular Simulation, Machine Learning and Experiment

通过结合分子模拟、机器学习和实验来开发和理解导热聚合物

• 批准号: 2332270
• 负责人: Tengfei Luo
• 金额: $ 40.57万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2332270&HistoricalAwards=false
• 关键词: Developing; Understanding; Thermally; Conductive; Polymers

Bulk amorphous polymers are usually thermal insulators with thermal conductivity (TC) in the range of 0.1-0.5 W/mK. However, they are widely used in heat transfer applications such as plastic heat exchangers, electronic cooling, and electric vehicle thermal management due to their mechanical flexibility and low cost. However, the development of thermally conductive amorphous polymers has been exceptionally challenging due to the lack of physical guidance. This project aims to combine molecular simulations, machine learning (ML), and experiments to develop amorphous polymers with high TC (1.5 W/mK) and understand the underlying molecular features dictating thermal transport. The methodology developed and the physics understood from this project will significantly speed up and increase the success rate of developing high TC polymers. These new materials will contribute to tackling the challenges in many heat transfer applications. This project will also provide multi-disciplinary education opportunities to students and researchers from diverse backgrounds and scientific fields. The topics of this project will cultivate future workforce for the U.S. industry. The goal of this project is to significantly speed up the development of thermally conductive polymer and understand the underlying structure-property relationships governing amorphous polymer TC. To reach this goal, the objectives of this project are: (1) establish a standardized polymer database using high-throughput molecular dynamics (MD) simulations, complemented by data from existing database (e.g., PolyInfo) and open literature; (2) employ a unique ML training technique, semi-supervised framework for graph imbalanced regression (SGIR), to develop accurate surrogate models that map out structure-property relations for polymer chemistry, structure and TC, and use our Graph Rationalization with Environment-based Augmentations (GREA) technique to identify the influential molecular features impacting TC; (3) use the established model to predict ~100 polymers with varying TC and perform detailed MD simulations to calculate the TC to verify the prediction and understand the ML-identified structure-property relationship; (4) down-select 10 polymers with the expert opinion from collaborating polymer chemists on the synthesizability of the predicted polymers, synthesize them and measure their TC. The data-driven approach to be established in this project will provide an impactful example in the field concerning thermally conductive polymers. The established protocol can be followed to design materials with other desirable properties, impacting science and engineering fields beyond polymer or thermal transport.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.

散装无定形聚合物通常是导热率（TC）的热绝缘子，范围为0.1-0.5 w/mk。但是，由于机械柔韧性和低成本，它们被广泛用于传热应用，例如塑料热交换器，电子冷却和电动汽车热管理。但是，由于缺乏身体指导，热导电无定形聚合物的发展非常具有挑战性。该项目旨在结合分子模拟，机器学习（ML）和实验，以开发具有高TC（1.5 w/mk）的无定形聚合物，并了解指示热传输的基本分子特征。开发的方法和从该项目中理解的物理学将显着加快加速，并提高开发高TC聚合物的成功率。这些新材料将有助于解决许多传热应用中的挑战。该项目还将为来自不同背景和科学领域的学生和研究人员提供多学科的教育机会。该项目的主题将为美国行业培养未来的劳动力。该项目的目的是显着加快导热聚合物的开发，并了解控制无定形聚合物TC的基本结构 - 质体关系。为了实现这一目标，该项目的目标是：（1）使用高通量分子动力学（MD）模拟建立标准化的聚合物数据库，并在现有数据库（例如PolyInfo）和开放文献的数据中得到补充； （2）采用独特的ML训练技术，用于图形不平衡回归（SGIR）的半监督框架（SGIR）来开发准确的替代模型，以绘制出用于聚合物化学，结构和TC的结构 - 特性关系，并将我们的图形合理化用于环境基于环境的增强技术（GREA）技术（GREA）技术，以识别有影响力的分子特征TC TC TC TC TC TC； （3）使用已建立的模型预测约100个具有不同TC的聚合物，并执行详细的MD模拟来计算TC以验证预测并了解ML识别的结构 - 托管关系； （4）下选择的10个聚合物，与聚合物化学家有关预测聚合物的合成性的专家意见，合成它们并测量其TC。该项目中要建立的数据驱动方法将在该领域提供有关导电聚合物的有影响力的例子。既定的协议可以遵循具有其他理想特性的设计材料，从而影响了聚合物或热运输之外的科学和工程领域。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准的评估来通过评估来支持的。