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Heterogeneous nucleation is the dominant form of liquid-to-solid transition in na- ture. Although molecular simulations are most uniquely suited to study nucleation, the waiting time to observe even a single nucleation event can easily exceed current computational capabilities. Therefore, there exists an imminent need for methods that enable computationally fast and feasible studies of heterogeneous nucleation. Seeding is a technique that has proven successful at dramatically expanding the range of computationally accessible nucleation rates in simulation studies of ho- mogeneous crystal nucleation. In this paper, we introduce a new seeding method for heterogeneous nucleation called Rigid Seeding (RSeeds). Crystalline seeds are treated as pseudo-rigid bodies and simulated on a surface with metastable liquid above its melting temperature. This allows the seeds to adapt to the surface and identify favorable seed–surface configurations, which is necessary for reliable predictions of crystal polymorphs that form and the corresponding heterogeneous nucle- ation rates. We demonstrate and validate RSeeds for heterogeneous ice nucleation on a flexible self-assembled monolayer surface, a mineral surface based on kaolinite, and two model surfaces. RSeeds predicts the correct ice polymorph, exposed crystal plane, and rotation on the surface. RSeeds is semiquantitative and can be used to estimate the critical nucleus size and nucleation rate when combined with classical nucleation theory. We demonstrate that RSeeds can be used to evaluate nucleation rates spanning many orders of magnitude. 

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. 

Abstract We introduce a new Python interface for the Cassandra Monte Carlo software, molecular simulation design framework (MoSDeF) Cassandra. MoSDeF Cassandra provides a simplified user interface, offers broader interoperability with other molecular simulation codes, enables the construction of programmatic and reproducible molecular simulation workflows, and builds the infrastructure necessary for high‐throughput Monte Carlo studies. Many of the capabilities of MoSDeF Cassandra are enabled via tight integration with MoSDeF. We discuss the motivation and design of MoSDeF Cassandra and proceed to demonstrate both simple use‐cases and more complex workflows, including adsorption in porous media and a combined molecular dynamics – Monte Carlo workflow for computing lateral diffusivity in graphene slit pores. The examples presented herein demonstrate how even relatively complex simulation workflows can be reduced to, at most, a few files of Python code that can be version‐controlled and shared with other researchers. We believe this paradigm will enable more rapid research advances and represents the future of molecular simulations.