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
基本信息
- 批准号:2332270
- 负责人:
- 金额:$ 40.57万
- 依托单位:
- 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:
- 关键词:
项目摘要
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.
大块无定形聚合物通常是热绝缘体,导热系数 (TC) 在 0.1-0.5 W/mK 范围内。然而,由于其机械灵活性和低成本,它们广泛应用于传热应用,例如塑料热交换器、电子冷却和电动汽车热管理。然而,由于缺乏物理指导,导热非晶聚合物的开发一直极具挑战性。该项目旨在结合分子模拟、机器学习 (ML) 和实验来开发具有高 TC (1.5 W/mK) 的无定形聚合物,并了解决定热传输的潜在分子特征。该项目所开发的方法和所理解的物理原理将显着加快并提高高TC聚合物开发的成功率。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教育机会。该项目的主题将为美国工业培养未来的劳动力。该项目的目标是显着加快导热聚合物的开发,并了解控制非晶态聚合物 TC 的基本结构-性能关系。为了实现这一目标,该项目的目标是:(1)利用高通量分子动力学(MD)模拟建立标准化聚合物数据库,并辅以现有数据库(例如PolyInfo)和开放文献的数据; (2) 采用独特的机器学习训练技术,图不平衡回归的半监督框架 (SGIR),开发准确的替代模型,绘制出聚合物化学、结构和 TC 的结构-性质关系,并使用我们的图合理化与环境 -基于增强 (GREA) 技术来识别影响 TC 的有影响力的分子特征; (3) 使用建立的模型预测约 100 种具有不同 TC 的聚合物,并进行详细的 MD 模拟以计算 TC,以验证预测并了解 ML 识别的结构-性能关系; (4)根据合作高分子化学家对预测聚合物的可合成性的专家意见,筛选出10种聚合物,进行合成并测量其TC。该项目将建立的数据驱动方法将为导热聚合物领域提供一个有影响力的例子。可以遵循既定的协议来设计具有其他理想特性的材料,影响聚合物或热传输之外的科学和工程领域。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Tengfei Luo其他文献
Probabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process
等温化学蒸气渗透过程的概率物理集成神经微分建模
- DOI:
10.48550/arxiv.2311.07798 - 发表时间:
2023-11-13 - 期刊:
- 影响因子:0
- 作者:
Deepak Akhare;Zeping Chen;R. Gulotty;Tengfei Luo;Jian - 通讯作者:
Jian
Simultaneous Solar-Driven Seawater Desalination and Continuous Oil Recovery
同步太阳能驱动海水淡化和连续石油采收
- DOI:
10.2139/ssrn.4241776 - 发表时间:
2022-12-01 - 期刊:
- 影响因子:0
- 作者:
Shiwen Wu;Ruda Jian;Siyu Tian;Long Zhou;Tengfei Luo;Guoping Xiong - 通讯作者:
Guoping Xiong
Thermal conductivity of organic bulk heterojunction solar cells: anunusual binary mixing effect
- DOI:
10.1039/c4cp04099f - 发表时间:
2014-10 - 期刊:
- 影响因子:3.3
- 作者:
Zhi Guo;Doyun Lee;Joseph Strzalka;Haifeng Gao;Libai Huang;Ali M. Khounsary;Tengfei Luo - 通讯作者:
Tengfei Luo
The role of optical phonons in intermediate layer-mediated thermal transport across solid interfaces
- DOI:
10.1039/c7cp02982a - 发表时间:
2017-06 - 期刊:
- 影响因子:3.3
- 作者:
Eungkyu Lee;Tengfei Luo - 通讯作者:
Tengfei Luo
An Ultra-soft Thermal Diode
超软热敏二极管
- DOI:
10.1016/j.mtphys.2024.101450 - 发表时间:
2023-01-11 - 期刊:
- 影响因子:11.5
- 作者:
Yunsong Pang;Junhong Li;Zhibin Wen;Ting Liang;Shan Gao;Min Yang;Dezhao Huang;Jianbin Xu;Tengfei Luo;Xiaoliang Zeng;Rong Sun - 通讯作者:
Rong Sun
Tengfei Luo的其他文献
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{{ truncateString('Tengfei Luo', 18)}}的其他基金
Collaborative Research: Material Simulation-driven Electrolyte Designs in Intermediate-temperature Na-K / S Batteries for Long-duration Energy Storage
合作研究:用于长期储能的中温Na-K / S电池中材料模拟驱动的电解质设计
- 批准号:
2341995 - 财政年份:2024
- 资助金额:
$ 40.57万 - 项目类别:
Standard Grant
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ISS:微重力下等离子气泡实现纳米颗粒沉积
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2224307 - 财政年份:2022
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$ 40.57万 - 项目类别:
Standard Grant
US-Japan Joint Workshop on Thermal Transport, Materials Informatics and Quantum Computing
美日热传输、材料信息学和量子计算联合研讨会
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2102592 - 财政年份:2021
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- 批准号:
1949910 - 财政年份:2020
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$ 40.57万 - 项目类别:
Standard Grant
EAGER: Collaborative Research: Dynamics of Nanoparticles in Light-Excited Supercavitation
EAGER:合作研究:光激发超空化中纳米粒子的动力学
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2040565 - 财政年份:2020
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合作研究:利用分子功能化来调节纳米级界面能量和动量传输
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2001079 - 财政年份:2020
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Continuing Grant
Collaborative Research: Understanding the Synergistic Effect of Graphene Plasmonics and Nanoscale Spatial Confinement on Solar-Driven Water Phase Change
合作研究:了解石墨烯等离子体和纳米尺度空间约束对太阳能驱动水相变的协同效应
- 批准号:
1937923 - 财政年份:2020
- 资助金额:
$ 40.57万 - 项目类别:
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Highly Sensitive Multiplexed Nanocone Array for Point-of-Care Pan-Cancer Screening
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1706039 - 财政年份:2017
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$ 40.57万 - 项目类别:
Standard Grant
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