Optimized performance of membrane-based desalination by high-throughput molecular dynamic simulations and machine learning analysis

The influence of pore size and hydrophilicity on the permeance of reverse osmosis (RO) membranes has been mostly focused. However, their influence is hardly to be clearly identified as these two kinds of factor interfere with each other. In this work, high-throughput molecular dynamics (HTMD) simulations with CNTs are used to extensively produce the data of water permeance and NaCl rejection. These data are then analyzed by machine learning (ML) method to obtain the optimized desalination performance. The HTMD results indicate that the pressure drop has little effect on the water permeance. Moreover, rising pore size and degrading hydrophilicity will generally boost water permeance but will somehow sacrifice the NaCl rejection. The interference effect between pore size and hydrophilicity is also found in this work, the mechanism of which is then revealed from molecular level. Additionally, ML is applied to analyze the abundant data of water permeance and NaCl rejection. The optimal conditions are identified to achieve the highest water permeance with 100% NaCl rejection, which are also validated via additional MD simulations. This work suggests that the integration of HTMD and ML promises the future of designing new kind of RO membranes for better performance.