School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Environ Sci Technol. 2022 Feb 15;56(4):2572-2581. doi: 10.1021/acs.est.1c04373. Epub 2021 Dec 30.
Polymeric membrane design is a multidimensional process involving selection of membrane materials and optimization of fabrication conditions from an infinite candidate space. It is impossible to explore the entire space by trial-and-error experimentation. Here, we present a membrane design strategy utilizing machine learning-based Bayesian optimization to precisely identify the optimal combinations of unexplored monomers and their fabrication conditions from an infinite space. We developed ML models to accurately predict water permeability and salt rejection from membrane monomer types (represented by the Morgan fingerprint) and fabrication conditions. We applied Bayesian optimization on the built ML model to inversely identify sets of monomer/fabrication condition combinations with the potential to break the upper bound for water/salt selectivity and permeability. We fabricated eight membranes under the identified combinations and found that they exceeded the present upper bound. Our findings demonstrate that ML-based Bayesian optimization represents a paradigm shift for next-generation separation membrane design.
聚合物膜设计是一个多维度的过程，涉及从无限的候选空间中选择膜材料和优化制造条件。通过试错实验不可能探索整个空间。在这里，我们提出了一种利用基于机器学习的贝叶斯优化策略，从无限的空间中精确地确定未探索单体及其制造条件的最佳组合的膜设计策略。我们开发了 ML 模型，以准确预测从膜单体类型（由 Morgan 指纹表示）和制造条件中得到的水透过率和盐截留率。我们在构建的 ML 模型上应用了贝叶斯优化，以反向识别具有突破水/盐选择性和渗透性上限潜力的单体/制造条件组合。我们在确定的组合下制造了八张膜，并发现它们超过了目前的上限。我们的研究结果表明，基于 ML 的贝叶斯优化代表了下一代分离膜设计的范式转变。
