Abstract Predicting gas permeabilities of polymers a priori is a long-standing challenge within the membrane research community that has important applications for membrane process design and ultimately widespread adoption of membrane technology. From early attempts based on free volume and cohesive energy to more recent group contribution methods, the ability to predict membrane permeability has improved in terms of accuracy. However, these models usually stay “within the paper”, i.e. limited model details are provided to the wider community such that adoption of these predictive platforms is limited. In this work, we combined an advanced polymer chemical structure fingerprinting method with a large experimental database of gas permeabilities to provide unprecedented prediction precision over a large range of polymer classes. No prior knowledge of the polymer is needed for the prediction other than the repeating unit chemical formula. In addition, we have incorporated this model into the existing Polymer Genome project to make it open to the membrane research community.

中文翻译：

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基于聚合物基因组的聚合物气体渗透性预测

摘要 先验预测聚合物的气体渗透率是膜研究界长期存在的挑战，它对膜工艺设计和膜技术的最终广泛采用具有重要应用。从基于自由体积和内聚能的早期尝试到最近的组贡献方法，预测膜渗透性的能力在准确性方面有所提高。然而，这些模型通常“在论文中”，即向更广泛的社区提供有限的模型细节，从而限制了这些预测平台的采用。在这项工作中，我们将先进的聚合物化学结构指纹识别方法与大型气体渗透率实验数据库相结合，以提供对大范围聚合物类别的前所未有的预测精度。除了重复单元化学式外，预测不需要聚合物的先验知识。此外，我们已将此模型纳入现有的聚合物基因组项目，以使其对膜研究界开放。