PUCHIK: A Python Package To Analyze Molecular Dynamics Simulations of Aspherical Nanoparticles

Abstract

Accurately describing a nanoparticle's interface is crucial for understanding its internal structure, interfacial properties, and ultimately, its functionality. While current computational methods provide reasonable descriptions for spherical and quasi-spherical nanoparticles, there remains a need for effective models for aspherical structures such as capsules and rod-like systems. This work introduces Python Utility for Characterizing Heterogeneous Interfaces and Kinetics (PUCHIK), a novel algorithm developed to describe both spherelike and aspherical nanoparticles. With an accurate description of the location of the interface of the nanoparticle, this algorithm then allows for various other important quantities (e.g., densities of different atom/molecule types relative to the interface, volume of the nanoparticle, amount of solubilized molecules within the nanoparticle) to be calculated. Our software development, we focused on providing good performance to computationally demanding projects, while ensuring that the methodological approach can be adapted as a protocol for other code implementations.

Figure 1

(a) Test structures with known density and volume: cylinder (left) and sphere (right). (b) Their corresponding number of atoms per volume as a function of the distance from the interface (negative values are inside the core of the nanoparticle) estimated by a standard approach (left) and the PUCHIK algorithm (right).

Figure 2

(a) Snapshot of an elongated TX100 micelle. Number of atoms per volume of water, hydrophobic tail, and hydrophilic head of Triton X-100 surfactant, as a function of distance from the interface of the hydrophobic core of the micelle. Comparison of results from two algorithms: (b) nanoCISC and (c) PUCHIK.

Figure 3

Same nanoparticle modeled using (a) a convex hull and (b) an alpha shape. Number of atoms per volume of water, hydrophobic tail, and hydrophilic head calculated using (c) a convex hull and (d) an alpha shape. r is the distance of the particle from the convex hull/alpha shape.

Figure 4

Linear dependence of the execution time on the number of particles.