Computational prediction of solvation structures in calcium battery electrolytes

Calcium ion batteries are emerging as a key focus in the pursuit of alternatives to lithium-ion batteries. However, a crucial gap remains in understanding how different electrolyte species influence their solvation structures. In this study, we demonstrate a comprehensive predictive approach that integrates ab initio calculations and machine learning force fields (MLFFs) to address this challenge. Using ab initio molecular dynamics (AIMD) simulations, we accurately predict the solvation structures within the first solvation shell, while also evaluating their reductive and oxidative stability through frontier orbital analysis. This analysis compares both implicit and explicit electrolyte conditions. To further elucidate these structures, we calculate and visualize their formation free energies using density functional theory (DFT), combined with heat map analysis. Additionally, MLFF simulations extend our predictions to nanosecond-scale trajectories, surpassing the limitations of picosecond-scale AIMD. The predicted solvated structures show strong agreement with both AIMD and DFT results, demonstrating the robustness of our approach. Thus, by leveraging these comprehensive methods, we provide a more reliable framework for predicting solvation structures in calcium ion and other battery electrolytes.