机器学习方法能否指导气体分离膜的制作?

IF 4.9 Q1 ENGINEERING, CHEMICAL
Arash Tayyebi , Ali S. Alshami , Xue Yu , Edward Kolodka
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引用次数: 12

摘要

将大量候选材料转化为具有合适形态和改进分子分离性能的膜对膜科学家来说是一项艰巨而昂贵的努力。随着近年来人工智能和机器学习的进步,机器学习方法能否指导气体分离膜的制造是一个及时的问题。答案是肯定的,本文通过系统地回顾和分析该领域最新的研究成果来解释这一答案的理由。这项工作旨在探索机器学习算法的潜力,作为一种有效和节省成本的工具,指导开发下一代聚合物膜的实验过程,并解决该领域的关键需求。研究结果表明,训练异聚物而不是均聚物,通过反设计方法合成新型聚合物,以及使用在相同条件下创建的可靠数据集,是实现设计意图的最关键因素。为任何打算在膜合成过程中使用ML算法的人提供了从A到Z的路径。文章最后简要讨论了未来的发展前景和尚未解决的ML驱动聚合物基膜设计和优化的开放性问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Can machine learning methods guide gas separation membranes fabrication?

Can machine learning methods guide gas separation membranes fabrication?

Transforming a vast array of candidate materials into membranes with suitable morphologies and improved molecular separation performance is an arduous and costly endeavor for membrane scientists. With the advancement made in artificial intelligence and machine-learning in recent years, it is timely to ask: can machine learning methods guide gas separation membranes Fabrication? The answer is “YES”, and this article explains the justifications for this answer by systematically reviewing and analyzing the up-to-date research efforts in the field. This work aimed to explore the potential of ML algorithms as an effective and cost-saving tool in guiding the experimental process of developing the next generation polymeric membranes, and in addressing the critical needs in the field. Findings demonstrate that training Heteropolymers instead of Homopolymers, synthesizing novel polymers by an inverse design approach, and using reliable datasets that are created under the same conditions, are the most crucial factors to achieve the design intent. A path from A to Z for anyone who intends to use ML algorithms in the membranes’ synthesis process is offered. The article concludes with a brief discussion on future development prospects and open issues that are yet to be addressed for ML‐driven polymeric‐based membranes design and optimization.

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