In the portfolio of technologies available for net zero-enabling solutions, such as carbon capture and low-carbon production of hydrogen, membrane-based gas separation is a sustainable alternative to energy-intensive processes, such as solvent-based absorption or cryogenic distillation. Detailed knowledge of membrane materials performance in wide operative ranges is a necessary prerequisite for the design of efficient membrane processes. With the increasing popularization of data-driven methods in natural sciences and engineering, the investigation of their potential to support materials and process design for gas separation with membranes has received increasing attention, as it can help compact the lab-to-market cycle. In this work we review several machine learning (ML) strategies for the estimation of the gas separation performance of polymer membranes. New hybrid modelling strategies, in which ML complements physics-based models and simulation methods, are also discussed. Such strategies can enable the fast screening of large databases of existing materials for a specific separation, as well as assist in de-novo materials design. We conclude by highlighting the challenges and future directions envisioned for the ML-assisted design and optimization of membrane materials and processes for traditional, as well as new, membrane separations.

中文翻译：

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从分子结构到工业性能，数据驱动的气体分离聚合物膜筛选和发现的视角

在可用于净零解决方案的技术组合中，例如碳捕获和低碳氢气生产，基于膜的气体分离是能源密集型工艺（例如基于溶剂的吸收或低温蒸馏）的可持续替代方案。详细了解膜材料在广泛操作范围内的性能是设计高效膜工艺的必要先决条件。随着数据驱动方法在自然科学和工程领域的日益普及，对其支持膜气体分离材料和工艺设计的潜力的研究受到越来越多的关注，因为它有助于缩短实验室到市场的周期。在这项工作中，我们回顾了几种用于估计聚合物膜气体分离性能的机器学习（ML）策略。还讨论了新的混合建模策略，其中机器学习补充了基于物理的模型和模拟方法。此类策略可以快速筛选现有材料的大型数据库以进行特定分离，并有助于从头开始材料设计。最后，我们强调了传统和新型膜分离的膜材料和工艺的机器学习辅助设计和优化所设想的挑战和未来方向。