Modeling aggregation processes of Lennard-Jones particles via stochastic networks

TL;DR

This paper models the aggregation of Lennard-Jones particles using stochastic networks to analyze transition probabilities, configurations, and structural preferences during cluster growth up to 14 atoms.

Contribution

It introduces a novel network-based approach to model and analyze the transient dynamics of particle aggregation, including the effects of attachment rates and temperature.

Findings

Icosahedral packing dominates small clusters.

Identified most likely configurations during aggregation.

Analyzed the impact of attachment rate and temperature on cluster formation.

Abstract

We model an isothermal aggregation process of particles/atoms interacting according to the Lennard-Jones pair potential by mapping the energy landscapes of each cluster size $N$ onto stochastic networks, computing transition probabilities {from} the network for an $N$-particle cluster to the one for $N+1$, and connecting these networks into a single joint network. The attachment rate is a control parameter. The resulting network representing the aggregation of up to 14 particles contains {6427} vertices. It is not only time-irreversible but also reducible. To analyze its transient dynamics, we introduce the sequence of the expected initial and pre-attachment distributions and compute them for a wide range of attachment rates and three values of temperature. As a result, we find the {configurations most likely to be observed} in the process of aggregation for each cluster size. We examine the attachment process and conduct a structural analysis of the sets of local energy minima for every cluster size. We show that both processes taking place in the network, attachment and relaxation, lead to the dominance of icosahedral packing in small (up to 14 atom) clusters.