A molecular dynamics study of water crystallization using deep neural network potentials of ab-initio quality

We study the crystallization of water into hexagonal ice (Ih) using molecular dynamics simulations. We describe the complex interactions between water molecules using deep neural network potentials[1] and employ state of the art enhanced sampling methods[2] to convert reversibly liquid water into ice Ih. From the simulations we calculate the difference in free energy between these two phases. The ice Ih configurations that emerge contain proton disorder as observed in experiments[3]. The proton disorder has an important contribution to the entropy of the solid[4] that most free energy methods are unable to capture. We assess whether our technique is able to capture it and we study the effect of the interaction potential in the proton disorder.

[1] L. Zhang, J. Han, H. Wang, R. Car, and W. E, Phys. Rev. Lett. 120, 143001 (2018)
[2] P. M. Piaggi and M. Parrinello, J. Chem. Phys. 150 (24), 244119 (2019)
[3] W. F. Giauque and J. W. Stout, J. Am. Chem. Soc. 58, 7, 1144 (1936)
[4] L. Pauling, J. Am. Chem. Soc. 57, 12, 2680 (1935)