Is the Local Ion Density Sufficient to Drive NaCl Nucleation from the Melt and Aqueous Solution?

Abstract

Even though nucleation is ubiquitous in different science and engineering problems, investigating nucleation is extremely difficult due to the complicated ranges of time and length scales involved. In this work, we simulate NaCl nucleation in both molten and aqueous environments using enhanced sampling all-atom molecular dynamics with deep learning-based estimation of reaction coordinates. By incorporating various structural order parameters and learning the reaction coordinate as a function thereof, we achieve significantly improved sampling relative to traditional ad hoc descriptions of what drives nucleation, particularly in the aqueous medium. Our results reveal a one-step nucleation mechanism in both environments, with reaction coordinate analysis highlighting the importance of local ion density in distinguishing solid and liquid states. However, while fluctuations in the local ion density are necessary to drive nucleation, they are not sufficient. Our analysis shows that near the transition states, descriptors such as enthalpy and local structure become crucial. Our protocol proposed here enables robust nucleation analysis and phase sampling, and could offer insights into nucleation mechanisms for generic small molecules in different environments.

References in corpus (14)

• Canonical sampling through velocity-rescaling
• Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method
• Crystal Nucleation in Liquids: Open Questions and Future Challenges in Molecular Dynamics Simulations
• The first-principles phase diagram of monolayer nanoconfined water
• Rate of Homogeneous Crystal Nucleation in molten NaCl
• State Predictive Information Bottleneck
• Ice Formation on Kaolinite: Insights from Molecular Dynamics Simulations
• Nucleation in aqueous NaCl solutions shifts from 1-step to 2-step mechanism on crossing the spinodal
• Driving and characterizing nucleation of urea and glycine polymorphs in water
• Reaction coordinates and rate constants for liquid droplet nucleation: quantifying the interplay between driving force and memory
• The kinetics of the ice-water interface from ab initio machine learning simulations
• Recent advances in describing and driving crystal nucleation using machine learning and artificial intelligence
• Nucleating a Different Coordination in a Crystal under Pressure: A Study of the B1-B2 Transition in NaCl by Metadynamics
• Thermodynamics-inspired Explanations of Artificial Intelligence

Cited by in corpus (4)

• Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
• Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation
• Atomic scale insights into NaCl nucleation in nanoconfined environments
• Thermodynamically Optimized Machine-learned Reaction Coordinates for Hydrophobic Ligand Dissociation