[Paper Review] Crumbling Crystals: On the Dissolution Mechanism of NaCl in Water

Niamh O’Neill, Christoph Schran+2 more|arXiv (Cornell University)|Nov 8, 2022

Spectroscopy and Quantum Chemical Studies4 citations

TL;DR

This study uses machine learning potentials to simulate NaCl dissolution in water at ab initio accuracy, revealing a stochastic, ion-by-ion unwrapping mechanism preceding rapid crystal disintegration—termed 'crumbling.' The process is driven by increasing surface-to-volume ratio, offering the first atomistic-resolution view of dissolution dynamics and enabling future studies of complex electrolyte systems under confinement or flow.

ABSTRACT

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.

Motivation & Objective

• To resolve the atomistic mechanism of NaCl dissolution in water, which remains poorly understood despite its biological and technological importance.
• To overcome limitations of prior simulations that relied on force fields or single trajectories, by enabling long-timescale, high-accuracy simulations of multiple dissolution events.
• To investigate whether dissolution follows a predictable, deterministic path or is inherently stochastic, especially under varying concentration and temperature conditions.
• To establish a generalizable methodology for studying complex dissolution processes in aqueous electrolytes, including those under confinement or flow.
• To provide a foundation for understanding dissolution in other ionic crystals and at water/salt interfaces relevant to battery science, desalination, and geochemistry.

Proposed method

• Employed machine learning potentials (MLPs) trained via active learning on ab initio molecular dynamics (AIMD) data using the rev-PBE functional with D3 dispersion correction.
• Used a Coulomb baseline with TIP3P water model and point charges for Na⁺ and Cl⁻ to ensure accurate long-range electrostatics via particle mesh Ewald summation.
• Iteratively improved the MLP model over multiple generations, achieving 1.3 meV/atom energy RMSE and 38.0 meV/Å force RMSE on the training set.
• Performed over 300 ns of NVT ensemble molecular dynamics simulations using CP2K/Quickstep at 330 K, with 10 independent trajectories per system to capture stochastic behavior.
• Simulated NaCl nanocrystals of varying sizes (4×4×4, 6×6×6) at different concentrations (1.43–5.61 mol/kg) and temperatures (including 400 K for validation).
• Analyzed trajectories to track ion loss, surface evolution, and structural disintegration, focusing on surface-to-volume ratio trends and ion-by-ion dissolution sequences.

Experimental results

Research questions

• RQ1 What is the atomistic-scale mechanism of NaCl dissolution in water, and does it proceed via a deterministic or stochastic pathway?
• RQ2 How do surface-to-volume ratio changes correlate with the onset of rapid crystal disintegration ('crumbling') during dissolution?
• RQ3 To what extent do dissolution dynamics depend on concentration and temperature, and are the observed behaviors generalizable across systems?
• RQ4 Can machine learning potentials accurately capture the delicate balance of water-water and water-ion interactions during dissolution at ab initio accuracy and long timescales?
• RQ5 How do the microscopic sub-processes—such as ion ejection and surface vacancy formation—contribute to the overall dissolution kinetics?

Key findings

• The dissolution mechanism is best described as a steady, ion-wise unwrapping of the crystal surface, followed by a sudden, rapid disintegration resembling 'crumbling.'
• The onset of crumbling coincides with a sharp increase in the surface-to-volume ratio of the nanocrystal, indicating that geometric factors dominate the transition to rapid disintegration.
• Dissolution is inherently stochastic, with significant variation in ion loss sequences across independent trajectories, even under identical conditions.
• The surface-to-volume ratio increases by over 50% during the final 20% of dissolution, correlating with the onset of rapid ion ejection and structural collapse.
• The machine learning potential model achieved high accuracy (1.3 meV/atom energy RMSE, 38.0 meV/Å force RMSE), enabling over 300 ns of ab initio-quality simulation—far beyond typical AIMD limits.
• The methodology successfully captures complex, dynamic interactions between ions and water molecules, including transient solvation shells and defect-mediated ion release, across multiple concentrations and temperatures.