Identifying local structure in molecular simulations is of utmost importance. The most common existing approach to identify local structure is to calculate some geometrical quantity referred to as an order parameter. In simple cases order parameters are physically intuitive and trivial to develop (e.g., ion-pair distance), however in most cases, order parameter development becomes a much more difficult endeavor (e.g., crystal structure identification). Using ideas from computer vision, we adapt a specific type of neural network called a PointNet to identify local structural environments in molecular simulations. A primary challenge in applying machine learning techniques to simulation is selecting the appropriate input features. This challenge is system-specific and requires significant human input and intuition. In contrast, our approach is a generic framework that requires no system-specific feature engineering and operates on the raw output of the simulations, i.e., atomic positions. We demonstrate the method on crystal structure identification in Lennard-Jones (four different phases), water (eight different phases), and mesophase (six different phases) systems. The method achieves as high as 99.5% accuracy in crystal structure identification. The method is applicable to heterogeneous nucleation and it can even predict the crystal phases of atoms near external interfaces. We demonstrate the versatility of our approach by using our method to identify surface hydrophobicity based solely upon positions and orientations of surrounding water molecules. Our results suggest the approach will be broadly applicable to many types of local structure in simulations.

Heterogeneous ice nucleation is the primary pathway for ice formation. However, the detailed molecular mechanisms by which surfaces promote or hinder ice nucleation are not well understood. We present results from extensive molecular dynamics simulations of ice nucleation near modified silver iodide (AgI) surfaces. The AgI surfaces are modified to investigate the effects of the surface charge distribution on the rate of ice nucleation. We find that the surface charge distribution has significant effects on ice nucleation. Specifically, AgI surfaces with the positive charges above the negative charges in the surface promote ice nucleation, while ice nucleation is hindered for surfaces in which the negative charges are above or in-plane with the positive charges. The structure of water molecules in the interfacial region as measured by the orientations of the water molecules relative to the surface can explain the differences in the ice nucleation at the different surfaces. We suggest that the distributions of the orientations of the interfacial water molecules could be used more broadly as a measure of ice nucleating propensity.

The mechanism of nucleation of clathrate hydrates of a water-soluble guest molecule is rigorously investigated with molecular dynamics (MD) simulations. Results from forward flux sampling, committor probability analysis, and twenty straightforward MD trajectories were combined to create a comprehensive understanding of the nucleation mechanism. Seven different classes of order parameters with a total of 33 individual variants were studied. We rank and evaluate the efficacy of prospective reaction coordinate models built from these order parameters and linear combinations thereof. Order parameters based upon water structuring provide a better approximation of the reaction coordinate than those based upon guest structuring. Our calculations suggest that the transition state is characterized by 2–3 partial, face-sharing 5¹² cages that form a structural motif observed in the structure II crystal. Further simulations show that once formed, this structure significantly affects the ordering of vicinal guest molecules, likely leading to hydrate nucleation. Our results contribute to the current understanding of the water–guest interplay involved in hydrate nucleation and have relevance to hydrate-based technologies that use water-soluble guest molecules (e.g., tetrahydrofuran) in mixed hydrate systems.