Prediction of UV-vis absorption spectra of thiolate-protected gold nanoclusters based on graph convolutional neural networks

clusters are represented as topological graphs and various features of the atomic surrounding environment are generated, including tensors such as the bond length and bond angle. The model framework can accurately predict the properties of nanoclusters with limited training data. Based on this neural network framework, we propose four extension methodologies for the precise prediction of the UV-vis absorption spectral curves of gold nanoclusters: prediction through key points, oscillator intensity, the discretized spectral curve, and encoder dimensionality reduction representation. We demonstrate the graph-based characterization method for gold nanoclusters and the ability of GCNN to predict complex targets, such as absorption spectra.