Discover and Understand Microporous Polymers for Size-sieving Separation Membranes using Active Learning

使用主动学习发现和了解用于尺寸筛分分离膜的微孔聚合物

• 批准号: 2102592
• 负责人: Tengfei Luo
• 金额: $ 42.7万
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102592&HistoricalAwards=false
• 关键词: Discover; Understand; Microporous; Polymers; Size

Membrane-based separation technologies have great promise to dramatically drive down the energy, carbon, and water intensity of traditional thermally-driven separation processes such as distillation. The creation of novel membrane materials with tailorable yet predictable structure and properties holds the key to providing low energy solutions to some of the nation’s most challenging and important separations, such as in clean energy industries (e.g., hydrogen gas purification) and environmental remediation (e.g., carbon capture). However, the development cycles of such materials are usually exceptionally long due to the requisite trial-and-error strategy employed. This project aims to combine simulations, machine learning, and experimental studies to accelerate the development of high-performance polymer gas separation membranes. The knowledge gained from this project will enable a more rational strategy for the design of advanced materials for energy-efficient separations and provide potentially revolutionary solutions to the grand materials challenges in the membrane separation field. This project will also strengthen the multi-disciplinary collaboration for incorporating machine learning into polymeric membrane materials. In addition to advancing knowledge and technology, the research will be integrated with graduate and undergraduate student education and training opportunities and through local K-12 student and teacher outreach programs. The overarching goal of this project is to accelerate the discovery and enrich the fundamental understanding of highly permeable and selective polymer gas separation membranes using an active learning scheme that synergistically combines molecular simulations, machine learning, and experiments. The scope of this project will include (1) establishing a standardized polymer database by combining existing database, open literature, and high-throughput molecular simulations; (2) employing transfer learning, molecular simulations, and experiments to develop accurate surrogate models that map out chemistry-property relations for polymer gas separation membranes; (3) establishing a Bayesian Optimization framework to guide the iteration of transfer learning and experimental discoveries; and (4) using classification together with detailed molecular simulation and experimental study to understand molecular features impacting polymer free volume architecture and gas transport properties. The Materials Informatics-based active learning approach to be established in this project will be a valuable strategy for the field concerning polymer separation membranes, and it can be readily extended to design polymers with other desirable properties beyond gas separation, which can save the cost and time traditionally required for new material development in general.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.

基于膜的分离技术有望大幅降低传统热驱动分离过程（例如蒸馏）的能源、碳和水强度，具有可定制且可预测的结构和性能的新型膜材料的创造是提供低能耗的关键。为国家一些最具挑战性和最重要的分离提供能源解决方案，例如清洁能源行业（例如氢气净化）和环境修复（例如碳捕获）。然而，此类材料的开发周期通常非常长。该项目旨在结合模拟、机器学习和实验研究，加速高性能聚合物气体分离膜的开发，从该项目中获得的知识将为更合理的策略提供支持。除了先进的知识之外，该项目还将加强多学科合作，将机器学习融入到聚合物膜材料中。和技术，该研究将与研究生和本科生相结合学生教育和培训机会以及通过当地 K-12 学生和教师推广计划 该项目的总体目标是利用协同结合的主动学习计划，加速发现并丰富对高渗透性和选择性聚合物气体膜分离的基本理解。该项目的范围将包括（1）结合现有数据库、开放文献和高通量分子模拟建立标准化聚合物数据库；（2）采用迁移学习、分子模拟和实验来开发绘制聚合物气体分离膜的化学性质关系的准确替代模型；（3）建立贝叶斯优化框架来指导迁移学习和实验发现的迭代；（4）使用分类以及详细的分子模拟和实验研究来了解影响聚合物自由体积结构和气体传输特性的分子特征，该项目中建立的基于材料信息学的主动学习方法将是聚合物分离膜领域的一个有价值的策略，并且可以很容易地扩展到设计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点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

Graph Rationalization with Environment-based Augmentations

通过基于环境的增强进行图形合理化

• DOI: 10.1145/3534678.3539347
• 发表时间: 2022-08
• 期刊: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Liu, Gang;Zhao, Tong;Xu, Jiaxin;Luo, Tengfei;Jiang, Meng
• 通讯作者: Jiang, Meng