Tempering of Au nanoclusters: capturing the temperature-dependent competition among structural motifs

TL;DR

This study develops a computational method combining PTMD, HSA, and global optimization to analyze temperature-dependent structural motifs in Au nanoclusters, revealing size- and temperature-driven structural competition and transformations.

Contribution

The paper introduces a combined computational approach to accurately capture the temperature-dependent structural behavior of gold nanoclusters across a wide temperature range.

Findings

Global minimum structures dominate at certain sizes and temperatures.

Au201 undergoes a solid-solid transformation below 200 K.

PTMD and HSA agree at intermediate temperatures, but differ near melting.

Abstract

A computational approach to determine the equilibrium structures of nanoclusters in the whole temperature range from 0 K to melting is developed. Our approach relies on Parallel Tempering Molecular Dynamics (PTMD) simulations complemented by Harmonic Superposition Approximation (HSA) calculations and global optimization searches, thus combining the accuracy of global optimization and HSA in describing the low-energy part of configuration space, together with the PTDM thorough sampling of high-energy configurations. This combined methodology is shown to be instrumental towards revealing the temperature-dependent structural motifs in Au nanoclusters of sizes 90, 147, and 201 atoms. The reported phenomenology is particularly rich, displaying a size- and temperature-dependent competition between the global energy minimum and other structural motifs. In the case of Au$_{90}$ and Au$_{147}$, the global minimum is also the dominant structure at finite temperatures. In contrast, the Au$_{201}$ cluster undergoes a solid-solid transformation at low temperature (< 200 K). Results indicate that PTMD and HSA very well agree at intermediate temperatures, between 300 and 400 K. For higher temperatures, PTMD gives an accurate description of equilibrium, while HSA fails in describing the melting range. On the other hand, HSA is more efficient in catching low-temperature structural transitions. Finally, we describe the elusive structures close to the melting region which can present complex and defective geometries, that are otherwise difficult to characterize through experimental imaging.