Machine learning method to predict the interlayer sliding energy barrier of polarized MoS2 layers

Machine learning is an effective method to predict the potential energy surface of interlayer sliding between two-dimensional (2D) materials but with less density functional theory (DFT) calculation burden. In this study, the machine learning method has been used to predict the sliding energy barriers between two MoS2 layers with both parallel and anti-parallel stacking modes, respectively. The sliding energy barrier prediction of anti-parallel stacking modes with only structure factors as features deviates from the DFT calculations remarkably. Howev-er, the introduction of interlayer charge density as the prediction feature significantly improves the prediction accuracy of anti-parallel stacking modes. This study enlightens the precise prediction of sliding energy barriers of polarized 2D materials with machine learning method.