Prediction of Energy Levels for Cr XIV by using Artificial Intelligence Techniques

Traditional approaches for predicting atomic energy levels, such as experimental measurements and computationally demanding calculations, are time-consuming and complex. The emergence of artificial intelligence (AI) provides a promising alternative. This study explores the application of AI algorithms to predict atomic energy levels, which are fundamental to understanding atomic structure. We first employed a neural network, one of the most widely used predictive techniques, and then advanced to the XGBoost algorithm. XGBoost effectively captures the complex relationships between quantum numbers and electronic configurations, providing more accurate and efficient predictions than the neural network. To construct the dataset required for training and evaluation, we computed the energy levels of the sodium-like chromium ion (Z = 24, Cr XIV) using two established atomic codes. The first is the pseudo-relativistic Hartree–Fock code with configuration interaction and relativistic corrections (Cowan’s HFR method). The second is AUTOSTRUCTURE (AUTOS.), which incorporates Breit interactions and quantum electrodynamics contributions. In addition to energy levels, radiative lifetimes and Landé g-factors were also calculated. The predicted results were compared with previously reported data for validation. This research demonstrates the potential of AI-based methods in atomic data analysis and provides a foundation for further investigations in bridging atomic physics with artificial intelligence applications.