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.

膜分离技术在极大地降低传统热驱动分离过程(如蒸馏)的能量、碳和水强度方面前景广阔。具有可定制但可预测的结构和性能的新型膜材料的创造，是为国家一些最具挑战性和最重要的分离提供低能量解决方案的关键，例如在清洁能源行业(如氢气净化)和环境修复(如碳捕获)中。然而，由于采用了必要的试错策略，这类材料的开发周期通常非常长。该项目旨在将模拟、机器学习和实验研究相结合，以加快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设计节能分离的先进材料提供更合理的战略，并为膜分离领域的重大材料挑战提供潜在的革命性解决方案。该项目还将加强将机器学习纳入聚合物膜材料的多学科合作。除了推进知识和技术，这项研究还将与研究生和本科生的教育和培训机会相结合，并通过当地的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分子模拟、机器学习和实验的主动学习方案，加速发现和丰富对高渗透和选择性聚合物气体分离膜的基本了解。该项目的范围将包括(1)结合现有的数据库、开放文献和高通量分子模拟建立标准化的聚合物数据库；(2)利用转移学习、分子模拟和实验开发精确的替代模型，绘制聚合物气体分离膜的化学-性质关系；(3)建立贝叶斯优化框架以指导转移学习和实验发现的迭代；以及(4)使用分类结合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性质的分子特征。在这个项目中建立的基于材料信息学的主动学习方法对于聚合物分离膜领域将是一个有价值的战略，它可以很容易地扩展到设计除了气体分离之外具有其他理想性能的聚合物，这可以从总体上节省传统上新材料开发所需的成本和时间。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。

项目成果

Graph Rationalization with Environment-based Augmentations

• DOI: 10.1145/3534678.3539347
• 发表时间: 2022-06
• 期刊: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Gang Liu;Tong Zhao;Jiaxi Xu;Te Luo;Meng Jiang