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

基于膜的分离技术在大幅降低传统热驱动分离过程（如蒸馏）的能量、碳和水强度方面具有很大的潜力。创造具有可定制但可预测的结构和性质的新型膜材料是为国家一些最具挑战性和重要的分离提供低能耗解决方案的关键，例如在清洁能源行业（例如，氢气纯化）和环境修复（例如，碳捕获）。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特别长。该项目旨在将联合收割机模拟、机器学习和实验研究相结合，以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节能分离的先进材料的设计提供更合理的策略，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。该项目还将加强多学科合作，将机器学习纳入聚合物膜材料。除了推进知识和技术，研究将与研究生和本科生的教育和培训机会，并通过当地的K-12学生和教师推广计划相结合。该项目的总体目标是加速发现并丰富对高渗透性和选择性聚合物气体分离膜的基本理解，使用协同结合分子模拟，机器学习和实验的主动学习方案。本计画的内容包括：（1）结合现有的资料库、公开文献及高通量分子模拟，建立标准化的聚合物资料库;（2）利用迁移学习、分子模拟及实验，发展精确的替代模型，以描绘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，指导迁移学习和实验发现的迭代;以及（4）利用分类结合详细的分子模拟和实验研究来理解影响聚合物自由体积的分子特征结构和气体传输特性。在本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，并且它可以很容易地扩展到设计具有气体分离以外的其他期望性能的聚合物，该奖项反映了NSF的法定使命，并被认为值得支持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。

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.