Speaker
Description
Two-dimensional (2D) materials placed on top of bulk substrates form complex heterostructures with rich interfacial physics with potential use in various applications such as nanoelectronics, sensing, and energy conversion. While numerous prior works have studied 2D-2D heterostructures, there have been relatively fewer studies that explore interfaces of 2D-bulk material heterostructures. In this study, we employ density functional theory (DFT) and machine learning (ML) models to investigate charge redistribution at the interface of over 1000 2D Janus-bulk heterostructures. We investigate the effect of metallic versus semiconducting substrates through electronic and Bader charge analyses of DFT-computed data. We then gain fundamental insights into the interfacial physics of 2D-bulk heterostructures by training predictive ML models to determine structure-property relationships of charge transfer and dipole moments across the interface. This work expands the currently available data of 2D-bulk heterostructures, thus enabling their use within a wide range of electronic, quantum computing, sensing, and energy applications.
