Charge transfer at air-water interfaces: A machine learning potential-based molecular dynamics study

Zhiwei Wang; Zhaoan Chen; Junjie Fang; Shanchen Li; Wanqi Zhou; Hu Qiu

doi:10.1063/5.0273607

Charge transfer at air-water interfaces: A machine learning potential-based molecular dynamics study

J Chem Phys. 2025 Jun 28;162(24):244705. doi: 10.1063/5.0273607.

Authors

Zhiwei Wang¹, Zhaoan Chen¹, Junjie Fang¹, Shanchen Li¹, Wanqi Zhou¹, Hu Qiu¹

Affiliation

¹ Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education, State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

PMID: 40557900
DOI: 10.1063/5.0273607

Abstract

Water at interfaces plays a crucial role in many natural processes and industrial applications. However, the relationship between water's hydrogen bonding and charge transfer characteristics at these interfaces remains poorly understood. Here, we develop machine learning potentials at near density functional theory accuracy based on datasets generated with ab initio molecular dynamics simulations, enabling us to explore the structure and charge transfer at air-water interfaces. Our simulations reveal a non-uniform charge distribution along the interfacial normal direction: water molecules in the outermost layer in direct contact with the air tend to be positively charged, while those in a thin sub-interface layer are negatively charged. We further demonstrate that this uneven charge distribution arises from the donor-acceptor asymmetry of H-bonds among interfacial water molecules. These findings provide a detailed atomic-level insight into the charge transfer behaviors of water at interfaces.