  Life on Earth depends upon the dissolution of ionic salts in water,
particularly NaCl. However, an atomistic scale understanding of the process
remains elusive. Simulations lend themselves conveniently to studying
dissolution since they provide the spatio-temporal resolution that can be
difficult to obtain experimentally. Nevertheless, the complexity of various
inter- and intra-molecular interactions require careful treatment and long time
scale simulations, both of which are typically hindered by computational
expense. Here, we use advances in machine learning potential methodology to
resolve for the first time at an ab initio level of theory the dissolution
mechanism of NaCl in water. The picture that emerges is that of a steady
ion-wise unwrapping of the crystal preceding its rapid disintegration,
reminiscent of crumbling. The onset of crumbling can be explained by a strong
increase in the ratio of the surface to volume of the crystal. Overall,
dissolution is comprised of a series of highly dynamical microscopic
sub-processes, resulting in an inherently stochastic mechanism. These atomistic
level insights now pave the way for a general understanding of dissolution
mechanisms in other crystals, and the methodology is primed for more complex
systems of recent interest such as water/salt interfaces under flow and salt
crystals under confinement.
