Machine learning reveals refreshing understanding of confined water

A new study provides surprising behavior of hydrogen bonding of water confined in carbon nanotubes.

Lawrence Livermore National Laboratory (LLNL) scientists combined large-scale molecular dynamics simulations with machine learning interatomic potentials derived from first-principles calculations to examine the hydrogen bonding of water confined in carbon nanotubes (CNTs). They found that the narrower the diameter of the CNT, the more the water structure is affected in a highly complex and nonlinear fashion. The research appears on the cover of The Journal of Physical Chemistry Letters.

The hydrogen-bond network of confined water in nanopores deviates from the bulk liquid, yet looking into the changes is a significant challenge. In the recent study, the team computed and compared the infrared (IR) spectrum of confined water with existing experiments to reveal confinement effects.

“Our work offers a general platform for simulating water in CNTs with quantum accuracy on time and length scales beyond the reach of conventional first-principles approaches,” said LLNL scientist Marcos Calegari Andrade, lead author of the paper.

Among many nanoporous systems, CNTs represent an ideal model system for studying confinement effects.

“An improved understanding of hydrogen bonding in nano-pores is not only important to bridge knowledge gaps in the structure and dynamics of confined water but also promises to advance a wide range of technological applications, from energy storage and conversion to ion-selective membranes for water desalination,” said Anh Pham, a co-author of the paper. 

The team developed and applied a neural network interatomic potential to understand the hydrogen bonding of water confined in single-walled CNTs. This potential allowed an efficient examination of confined water for a wide range of CNT diameters at time and length scales beyond-reach of conventional first-principles approaches while retaining their computational accuracy.

Team members used molecular dynamics simulations to predict IR spectra of confined water at room temperature and compared them with existing experimental measurements to decipher the effects of confinement on hydrogen bonding. The simulations indicated that water undergoes an order–disorder transition inside CNTs with diameters of approximately 1.2 nanometers. For the wider CNTs, confinement imposes disruptive effects on the hydrogen-bond network of water, leading to an increased number of broken hydrogen-bonds and a more disordered water structure than for the bulk liquid.

“We also found previously unreported aspects of water confined in narrower CNTs, especially for those with diameters smaller than 1.2 nm. In contrast to the monotonic behavior of water found in wide CNTs, simulations with the machine learning potential indicate that confinement affects water structure in a highly complex and nonlinear fashion in these narrow CNT pores,” Calegari Andrade said.

The research is funded by the Center for Enhanced Nanofluidic Transport, an Energy Frontier Research Center funded by the Department of Energy, Office of Science, Basic Energy Sciences and the LLNL Grand Challenge Program.