Machine Learning Bandgap Prediction of Nanoporous Graphenes with Water

Abstract

The structure and dynamical behavior of water confined at or within nanostructures is a topic central to many fields, from biology to emerging electronics such as carbon nanostructures. Nanoporous graphene (NPG) containing periodic nanoscale pores with specific topologies has emerged as a promising material in carbon-based nanoelectronics; however, its interaction with ambient water remains poorly understood. Here, we combine density functional theory (DFT), ab initio molecular dynamics (AIMD), and interpretable machine learning (ML) to reveal how water controls quantum transport in NPGs. Depending on the local hydration structure, the bandgap varies by more than a factor of two across NPG and nitrogen-doped hybrid (h-NPG) systems. To uncover the underlying mechanism, we develop Smooth Overlap of Atomic Positions (SOAP)-based black-box and physics-informed grey-box ML models. The Gaussian process regression model achieves near-DFT accuracy while enabling physical interpretation. Analysis identifies water dipole orientation, water-substrate distance, water center-of-geometry, and ribbon-resolved dipole moments as the dominant factors controlling bandgap modulation across NPG and h-NPG systems.