Pervaporation (PV) is an effective membrane separation process for organic dehydration, recovery, and upgrading. However, it is crucial to improve membrane materials beyond the current permeability-selectivity trade-off. In this research, we introduce machine learning (ML) models to identify high-potential polymers, greatly improving the efficiency and reducing cost compared to conventional trial-and-error approach. We utilized the largest PV data set to date and incorporated polymer fingerprints and features, including membrane structure, operating conditions, and solute properties. Dimensionality reduction, missing data treatment, seed randomness, and data leakage management were employed to ensure model robustness. The optimized LightGBM models achieved RMSE of 0.447 and 0.360 for separation factor and total flux, respectively (logarithmic scale). Screening approximately 1 million hypothetical polymers with ML models resulted in identifying polymers with a predicted permeation separation index >30 and synthetic accessibility score <3.7 for acetic acid extraction. This study demonstrates the promise of ML to accelerate tailored membrane designs.

中文翻译：

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用于聚合物设计的机器学习以增强基于渗透蒸发的有机回收

渗透蒸发（PV）是一种有效的膜分离工艺，用于有机物脱水、回收和升级。然而，改进膜材料超越目前的渗透性-选择性权衡至关重要。在这项研究中，我们引入机器学习（ML）模型来识别高潜力聚合物，与传统的试错方法相比，大大提高了效率并降低了成本。我们利用了迄今为止最大的光伏数据集，并结合了聚合物指纹和特征，包括膜结构、操作条件和溶质特性。采用降维、缺失数据处理、种子随机性和数据泄漏管理来确保模型的稳健性。优化后的 LightGBM 模型的分离因子和总通量的 RMSE 分别为 0.447 和 0.360（对数标度）。使用 ML 模型筛选大约 100 万种假设聚合物，识别出乙酸萃取的预测渗透分离指数 >30 和合成可及性得分 <3.7 的聚合物。这项研究证明了机器学习在加速定制膜设计方面的前景。