Machine learning approaches for modelling of molecular polarizability in gold nanoclusters

The polarizability of molecules describes their response to an external electric field. It quantifies the ability of a system to form an induced dipole moment when subjected to an electric field. In this work, we investigated isotropic polarizability and anisotropy in the polarizability of gold nanoclusters using various machine-learning algorithms. We utilized high-order invariant descriptors based on spherical harmonics, integrated with machine-learning models like artificial neural network, Gaussian process regression, and kernel ridge regression. Our results demonstrate the efficacy of machine-learning in accurately predicting the polarizability of gold nanoclusters. We find that ANN-based model performs better than the other models. • We used different ML models to predict the polarizability of gold nanoclusters. • ANN, GPR, and KRR models were compared, with ANN achieving the best performance. • Bispectrum and power spectrum-based descriptors were used as input to ML models. • Performance of ANN with Kalman filter optimizer was better than Adam optimizer.