Charge govern ion-selective mechanism of YX2 desalination pore using high-throughput computations and machine learning

Energy-efficient and low-cost desalination of water continues to be a problem that society is facing. With the advances in nanotechnology, a variety of nanopore membranes have emerged for desalination. In particular, YX2 nanopores (YW, Mo, etc. and X = S, Se, Te, etc.) have been considered as promising membranes for water purification. In this work, we used a combination of high-throughput molecular dynamics simulations and machine learning to reveal the nanopore mass transfer mechanism and rationally design the YX2 nanopore. The pore size and the charge of the pore mouth were found to be the main factors affecting water permeation and salt rejection, as discovered through the machine learning study. The physical-chemical analysis provides us with a deep understanding of the charge-governed mechanism on the desalination performance of YX2 nanopores. It revealed that YX2 nanopores with a conical shape and minimized pore charges could be ideal candidates for excellent desalination performance. Based on this, we designed the YX2 pore with a specific charge distribution, which could achieve high water permeation with high salt rejection. Therefore, the combination of machine learning and high-throughput computation methods could aid in designing materials with excellent performance for desalination.