Enhancing the formation of ionic defects to study the ice Ih/XI transition with molecular dynamics simulations

Abstract

Ice Ih, the common form of ice in the biosphere, contains proton disorder. Its proton-ordered counterpart, ice XI, is thermodynamically stable below 72 K. However, the formation of ice XI is kinetically hindered, and experimentally it is obtained by doping with KOH. Doping creates ionic defects that promote the migration of protons and the associated change in proton configuration. In this article, we mimic the effect of doping with a bias potential that enhances the formation of ionic defects in molecular dynamics simulations. The recombination of the ions thus formed proceeds through fast migration of the hydroxide along hydrogen bond loops, providing a physical and expedite way to change the proton configuration. A key ingredient of this approach is a machine learning potential trained with density functional theory data and capable of modelling molecular dissociation. We exemplify the usefulness of this idea by studying the order-disorder transition using an appropriate order parameter that distinguishes the proton environments in ice Ih and XI. We calculate the changes in free energy, enthalpy, and entropy associated with the transition. Our estimated entropy agrees with experiment within the error bars of the calculation.

GRAPHICAL ABSTRACT

Acknowledgements

We are grateful to Marcos Calegari for providing the training data for the neural network to predict the positions of the Wannier centroids. P.M.P was supported by an Early Postdoc.Mobility fellowship from the Swiss National Science Foundation. This work was conducted within the center: Chemistry in Solution and at Interfaces funded by the DoE under Award DE-SC0019394. Simulations reported here were substantially performed using the Princeton Research Computing resources at Princeton University which is consortium of groups including the Princeton Institute for Computational Science and Engineering and the Princeton University Office of Information Technology's Research Computing department.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Swiss National Science Foundation [P2ELP2_184382] and U.S. Department of Energy [DE-SC0019394].