Correlation of structure with UV-visible spectra by varying SH composition in Au-SH nanoclusters

In the present work, we model artificial neural network (ANN) potentials for Au (SH) nanoclusters in the range of n = 10 to n = 38. The accuracy of ANN potentials is tested by comparing the global minimum (GM) structures of Au (SH) nanoclusters, at saturated amount of SH, with the earlier reported structures. The GM structures are reported for the first time for nanoclusters with compositions lower than the saturated SH composition. We calculate the probability of low energy isomers to explain the fluxional behaviour of Au (SH) nanoclusters at lower SH compositions. Furthermore, we try to correlate the structures of Au (SH) nanoclusters with UV-visible spectra based on Time-dependent density functional theory (TDDFT) calculations. The UV-visible spectral analysis reveals that significant spectroscopic variations are observed at different SH compositions. This study provides a fundamental understanding of structural changes with decreasing SH compositions and with increasing the size of the nanocluster.