Data-Driven Modeling and Prediction of Selective Transport in MXene Membrane, By Mojtaba Farrokhbin

Research

Title	Data-Driven Modeling and Prediction of Selective Transport in MXene Membrane
Type	Presentation
Keywords	Molecular Dynamics, MXene, Water Distillation, Ion Rejection
Researchers	hakimeh pozesh (First researcher) , Mojtaba Farrokhbin (Second researcher)

Abstract

Molecular dynamics simulations are used to study pressure-driven transport of saline water through a nanoporous MXene membrane. External pressure is applied via a graphene piston to examine water and ion permeation. The results show strong membrane selectivity, with water molecules permeating efficiently while ion transport is suppressed due to ion dehydration energy barriers. A simple machine-learning–based polynomial regression is applied to capture pressure dependent trends, yielding a strong correlation for water transport (R2 = 0.97) and a weaker, more variable correlation for ion transport (R^2 = 0.84). These findings provide molecular insight into selective transport in MXene membranes for desalination applications.