使用机器学习和麦克斯韦尔模型,为混合矩阵膜的MOF进行选
Xiaohan Yu1, Jia Yuan Chng1, David S Sholl2
1School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0100, United States.
概括
本研究介绍了一种灵活的金属有机框架 (MOF) 工作流程,使用机器学习来预测气体分离的混合矩阵膜性能,提高了对刚性模型的准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
背景情况:
- 基于金属有机框架 (MOF) 的混合矩阵膜 (MMM) 将MOF粒子集成到聚合物矩阵中,提供聚合物和无机膜的综合优势.
- 以前的MMM性能预测依赖于分子模拟中的刚性MOF假设,限制了准确性,特别是对于分子扩散度.
- 使用了机器学习 (ML) 和麦克斯韦模型,但没有考虑MOF框架的灵活性.
研究的目的:
- 开发一种新的工作流程,将ML模型与MOF灵活性集成在一起,以准确地预测MMM性能.
- 在Maxwell模型中分析可实现的MMM性能的全部范围.
- 为气体分离应用确定有前途的MOF.
主要方法:
- 开发了一个新的计算工作流程,将ML模型与灵活的MOF框架集成在一起.
- 在CO2/CH4,O2/N2和He/H2分离中预测的透性和选择性为131,722MMMs.
- 使用麦克斯韦尔模型分析了MMM性能,并结合了MOF灵活性.
主要成果:
- 成功预测了大型数据集 (131,722个组合) 的MMM性能.
- 确定了几种有前途的MOF用于各种气体分离应用.
- 证明了MOF灵活性对预测准确性的影响.
结论:
- 开发的灵活的MOF工作流提供了一个有效的工具来选MMM.
- 这种方法提高了对气体分离的MMM性能预测的准确性.
- 促进了用于工业应用的新MOF/聚合物组合的发现.
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