Theoretical Framework Based on Molecular Dynamics and Data Mining Analyses for the Study of Potential Energy Surfaces of Finite-Size Particles

Abstract

Nanoclusters are remarkably promising for the capture and activation of small molecules for fuel production or as precursors for other chemicals of high commercial value. Since this process occurs under a wide variety of experimental conditions, an improved atomistic understanding of the stability and phase transitions of these systems will be key to the development of successful technological applications. In this work, we proposed a theoretical framework to explore the potential energy surface and configuration space of nanoclusters to map the most important morphologies presented by those systems and the phase transitions between them. A fully automated process was developed, which combines global optimization techniques, classical molecular dynamics, and unsupervised machine learning algorithms. To showcase these capabilities of the approach, we explored the example of copper nanoclusters (Cu n ) where n = 13, 38, 55, 75, 98, 102, and 147. We not only reported a graphical potential energy surface for each size, but also explored the topology of the configuration space via structural and thermodynamic analyses. The effect of size on the potential energy surface and the critical temperature for solid−liquid phase transitions were also reported, highlighting the impact of magic numbers on those quantities.

Theoretical study of the structural and energetic properties of Ce1−xZrxO2 nanoparticles via molecular dynamics simulations

“…20 Structure reorganization is the collective behavior of all atoms within the NP, which depends on the internal energy, temperature, cooperative effects, critical points, and entropy contribution. 56 Those effects can yield a continuous or an abrupt (discontinuous) change in the structure, which is a challenge to characterize in finite-size NPs with about 1 to 10 nm in diameter. Abrupt changes in the structure ordering will affect the physical-chemical properties, e.g., internal energy, heat capacity, average coordination, etc.…”

Theory of Anisotropic Metal Nanostructures

“…Although the above discussion was heavily focused on Au nanocrystals, with good reason, as many cutting-edge theoretical studies have focused on Au, the nanocrystalline structures of other metals are not without interest. ,,,− I will highlight some of these studies below. Also, the studies discussed above all addressed clusters in vacuum.…”

Size and temperature dependent shapes of copper nanocrystals using parallel tempering molecular dynamics

“…[32][33][34] In a recent study, a theoretical framework was proposed to combine global optimization, classical molecular dynamics, and unsupervised machine learning algorithms to characterize the shapes of Cu nanocrystals. 35 In this work, we use Parallel Tempering Molecular Dynamics (PTMD) to probe the temperature-dependent, minimum free-energy shapes of Cu nanocrystals in the 100-200 atom size range. A few recent studies have used PTMD [36][37][38][39] or variants 39 to obtain temperature-dependent shape distributions of Au, [36][37][38] Cu, 38 and Ag 38,39 nanocrystals.…”

“…32–34 In a recent study, a theoretical framework was proposed to combine global optimization, classical molecular dynamics, and unsupervised machine learning algorithms to characterize the shapes of Cu nanocrystals. 35…”