Developing Machine Learning Potential to Unravel Quantum Effect on Ionic Hydration and Transport in Nanoscale Confinement

Nanoporous materials such as carbon nanotubes have served as the foundation for creating advanced separation technologies that sustainably support human society; for example, by producing clean water or recovering critical materials such as lithium. A unique structure-related property of nanoporous materials arises from their nanoscale pores, which exert quantum mechanical effects on the ions and molecules within the pores resulting in properties vastly differing from those observed in everyday bulk quantities.

A complete understanding of the nanoscale confinement-induced effects is crucial for rational design of separation technologies using nanoporous membranes. However, fundamental knowledge of the nanoscale confinement-induced quantum effects on the properties of ions and molecules is limited, hampering the development of separation technologies. This project aims to develop computational models to elucidate the quantum effects on the structural and dynamic properties of ions and molecules confined in carbon nanotubes.

The proposed research and education activities are closely integrated in this project through undergraduate research opportunities that focus on promoting diversity within the STEM field and the development of course content to train the future STEM workforce in advanced computational analysis techniques. This project is jointly funded by the Interfacial Engineering program of ENG/CBET and the Established Program to Stimulate Competitive Research (EPSCoR).

The properties of nanoconfined fluids have been topics for science and engineering investigation because of their importance in developing advanced separation and reaction technologies. This research has demonstrated that the key to understanding nanoconfined fluid behavior lies in the fluid’s non-bulk properties. Nanoscale confinement can incite quantum effects due to the physically constraining and anisotropic environment.

Quantum effects play an essential role in determining the non-bulk features of nanoconfined fluids. However, a knowledge gap remains in understanding the quantum effect on the non-bulk features of ions and molecules due to the lack of efficient computational tools. This research group hypothesizes that a well-trained machine learning potential can predict the hydration of ions in bulk and within carbon nanotubes as accurately as quantum mechanical calculations.

Driven by this hypothesis, this project will develop a machine learning-based force field and investigate the quantum effect on ionic hydration and transport in carbon nanotubes (CNT) over a range of diameters and chiralities. The project includes three research tasks: (1) develop a machine learning force field to investigate the hydration structure, dynamics, and thermodynamics of selected cations (Li+, Na+, K+, Mg2+ and H+) and anions (F-, and Cl-) in the bulk phase; (2) develop a machine learning force field to investigate the non-bulk properties of water molecules inside carbon nanotubes; and (3) extend the machine learning force field and investigate ionic hydration and transport effects in carbon nanotubes.

The proposed research will be conducted using molecular dynamics (MD), ab initio MD, enhanced sampling, and machine learning methods. The most significant outcomes will include (a) a deeper understanding of the quantum effect on ionic hydration and transport in the nanoscale confinement and (b) an extensible machine learning force field for ions and water molecules in the bulk phase and inside CNTs.

The outcomes will provide the knowledge and cyber-infrastructure for model-based design of nanoporous separation membranes.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.