Hydrogenation of C24 Carbon Clusters: Structural Diversity and Energetic Properties.

This work aims at exploring the potential energy surfaces of C24Hn=0,6,12,18,24 using the genetic algorithm in combination with the density functional based tight binding potential. The structural diversity was analyzed using order parameters, in particular the sum of the numbers of 5- and 6-carbon rings R5/6. The most abundant and lowest energy population was designated as the flake population (isomers of variable shapes, large R5/6 values), characterized by an increasing number of spherical isomers when nH/nC increases. Simultaneously, the fraction of the pretzel population (spherical isomers, smaller R5/6 values) increases. The fraction of the cage population (largest R5/6 values) remains extremely minor while the branched population (smallest R5/6 values) remains the highest energy population for all nH/nC ratios. For all C24Hn=0,6,12,18,24 clusters, the evolution of the carbon ring size distribution with energy clearly shows the correlation between the stability and the number of 6-carbon rings. The average values of the ionization potentials of all populations were found to decrease when nH/nC increases, ranging from 7.9 down to 6.4 eV. This trend was correlated to geometric and electronic factors, in particular to carbon hybridization. These results are of astrophysical interest, especially regarding the role of carbon species in the gas ionization.

Infrared Spectroscopy of Chemically Diverse Carbon Clusters: A Data-Driven Approach.

Carbon clusters exhibit a broad diversity of topologies and shapes, encompassing fullerene-like cages, graphene-like flakes, and more disordered pretzel-like and branched structures. Here, we examine computationally their infrared spectra in relation with these structures from a statistical perspective. Individual spectra for broad samples of isomers were determined by means of the self-consistent charge density functional-based tight-binding method, and an interpolation scheme is designed to reproduce the spectral features by regression on a much smaller subset of the sample. This interpolation proceeds by encoding the structures using appropriate descriptors and selecting them through principal component analysis, Gaussian regression or inverse distance weighting providing the nonlinear weighting functions. Metric learning is employed to reduce the global error on a preselected testing set. The interpolated spectra satisfactorily reproduce the specific spectral features and their dependence on the size and shape, enabling quantitative prediction away from the testing set. Finally, the classification of structures within the four proposed families is critically discussed through a statistical analysis of the sample based on iterative label spreading.