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

Absence of Amorphous Forms When Ice XIc Is Compressed at Low Temperature

It is generally believed that ice crystal-to-crystal transitions do not occur below the glass-transition temperature. For instance, under compression, ice I becomes a metastable state but does not transform into other high-pressure ice crystals, and applying excessive pressure ends up causing its collapse into high-density amorphous ice (HDA). Here, we perform molecular dynamics (MD) simulations to demonstrate that a hydrogen-ordered form of cubic ice (ice Ic) transforms to a hydrogen-ordered form of ice IV without yielding HDA. Our comprehensive search for different configurations combined with density functional theory (DFT) calculations indicates that the hydrogen-ordered ice IV formed by compression would be the thermodynamically most stable hydrogen-ordered ice IV configuration. We also predict the phase boundary of hydrogen order-disorder transition of ice IV, which remains unknown in experiments. Our findings suggest that hydrogen ordering enables an immediate transition between ice polymorphs that is impossible when the hydrogens are disordered.

Probing the self-ionization of liquid water with ab initio deep potential molecular dynamics

The chemical equilibrium between self-ionized and molecular water dictates the acid-base chemistry in aqueous solutions, yet understanding the microscopic mechanisms of water self-ionization remains experimentally and computationally challenging. Herein, Density Functional Theory (DFT)-based deep neural network (DNN) potentials are combined with enhanced sampling techniques and a global acid-base collective variable to perform extensive atomistic simulations of water self-ionization for model systems of increasing size. The explicit inclusion of long-range electrostatic interactions in the DNN potential is found to be crucial to accurately reproduce the DFT free energy profile of solvated water ion pairs in small (64 and 128 H2O) cells. The reversible work to separate the hydroxide and hydronium to a distance [Formula: see text] is found to converge for simulation cells containing more than 500 H2O, and a distance of [Formula: see text] 8 Å is the threshold beyond which the work to further separate the two ions becomes approximately zero. The slow convergence of the potential of mean force with system size is related to a restructuring of water and an increase of the local order around the water ions. Calculation of the dissociation equilibrium constant illustrates the key role of long-range electrostatics and entropic effects in the water autoionization process.

Homogeneous ice nucleation in an ab initio machine-learning model of water

Molecular simulations have provided valuable insight into the microscopic mechanisms underlying homogeneous ice nucleation. While empirical models have been used extensively to study this phenomenon, simulations based on first-principles calculations have so far proven prohibitively expensive. Here, we circumvent this difficulty by using an efficient machine-learning model trained on density-functional theory energies and forces. We compute nucleation rates at atmospheric pressure, over a broad range of supercoolings, using the seeding technique and systems of up to hundreds of thousands of atoms simulated with ab initio accuracy. The key quantity provided by the seeding technique is the size of the critical cluster (i.e., a size such that the cluster has equal probabilities of growing or melting at the given supersaturation), which is used together with the equations of classical nucleation theory to compute nucleation rates. We find that nucleation rates for our model at moderate supercoolings are in good agreement with experimental measurements within the error of our calculation. We also study the impact of properties such as the thermodynamic driving force, interfacial free energy, and stacking disorder on the calculated rates.

Ring population statistics in an ice lattice model

We calculate the distribution probability of hexagonal six-site rings in the disordered state of a cubic or hexagonal ice lattice model with ice rules perfectly obeyed. The mean-field distribution obtained is in significant agreement with those, slightly different among them, obtained by Monte Carlo simulations of the cubic or hexagonal model. The results are discussed in connection with the equilibrium and non-equilibrium transition from disorder to ferroelectric proton order.