Predicting the Optical Properties of Gold Nanoclusters Using Machine Learning Approach

The synthesis of gold nanoclusters (AuNC) is strongly influenced by various reaction conditions, and their optical properties are determined by factors such as the nature of the ligand and the measuring solvent, among others. To improve the efficiency of the synthesis of metallic gold nanoclusters with the desired functionality, the application of machine learning techniques is a smart choice. In this study, a model based on the GXBoost algorithm is proposed to predict the maximum emission wavelength of the AuNC emission from a database that includes more than 200 scientific articles. The validation of the model was carried out through the comparison of prediction versus experimental data (not included in the model) and the training and validation data. The model showed a percentage error of 1.7, 1.6, and 4.9%, respectively, indicating a reasonable return. Additionally, an independent regression was performed when the ligand was a thiolated compound different from GSH, obtaining a training and test error percentage of 0.01 and 3%, respectively. In addition, critical variables affecting the optical properties of nanoclusters were explored, and techniques such as One-Hot Encoding were used to prepare the data. Finally, this work not only underscores the relevance of AuNCs in modern science highlighted but also demonstrates the potential of machine learning as a predicting tool and design of materials in nanoscience, contributing to the optimization of their properties for future applications.