Jeong Nohyeong, Park Shinyun, Mahajan Subhamoy, Zhou Ji, Blotevogel Jens, Li Ying, Tong Tiezheng, Chen Yongsheng
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, 80523, USA.
Nat Commun. 2024 Dec 30;15(1):10918. doi: 10.1038/s41467-024-55320-9.
Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models. Utilizing the Shapley additive explanation method for XGBoost model interpretation unveils the impacts of both PFAS characteristics and membrane properties on model predictions. The examination of the impacts of chemical structure involves interpreting the multimodal transformer model incorporated with simplified molecular input line entry system strings through heat maps, providing a visual representation of the attention score assigned to each atom of PFAS molecules. Both ML interpretation methods highlight the dominance of electrostatic interaction in governing PFAS transport across polyamide membranes. The roles of functional groups in altering PFAS transport across membranes are further revealed by molecular simulations. The combination of ML with computer simulations not only advances our knowledge of PFAS removal by polyamide membranes, but also provides an innovative approach to facilitate data-driven feature selection for the development of high-performance membranes with improved PFAS removal efficiency.
全氟和多氟烷基物质（PFASs）最近因其对人类和生态健康的影响而备受关注。尽管聚酰胺膜在修复受PFASs污染的水方面发挥着重要作用，但影响PFASs跨膜传输的控制因素仍不明确。在本研究中，我们使用两种机器学习（ML）模型，即XGBoost和多模态变压器模型，研究聚酰胺膜对PFASs的截留情况。利用Shapley加性解释方法对XGBoost模型进行解释，揭示了PFASs特性和膜性能对模型预测的影响。对化学结构影响的考察包括通过热图解释结合简化分子输入线性输入系统字符串的多模态变压器模型，直观呈现分配给PFAS分子每个原子的注意力得分。两种ML解释方法都强调了静电相互作用在控制PFASs跨聚酰胺膜传输中的主导作用。分子模拟进一步揭示了官能团在改变PFASs跨膜传输中的作用。ML与计算机模拟的结合不仅增进了我们对聚酰胺膜去除PFASs的认识，还提供了一种创新方法，便于进行数据驱动的特征选择，以开发具有更高PFASs去除效率的高性能膜。
