Computational Prediction of Solvation Structure in Calcium Battery Electrolytes

Calcium ion batteries are gaining attention as a promising alternative to lithium-ion systems, but a significant gap exists in understanding how various electrolyte species affect their solvation structures. In this study, we present a comprehensive predictive framework that combines ab initio calculations with machine learning force fields (MLFF) to address this challenge. Through ab initio molecular dynamics (AIMD) simulations, we accurately predict the solvation structures within the first solvation shell and assess their reductive and oxidative stability using frontier orbital analysis under both implicit and explicit electrolyte conditions. To further analyze these structures, we calculate and visualize their formation free energies using density functional theory (DFT) paired with heat map analysis. Moreover, MLFF simulations extend these predictions to nanosecond timescales, overcoming the limitations of AIMD simulations, which are typically constrained to picosecond timescales. The predicted solvation structures show excellent agreement with both AIMD and DFT results, underscoring the reliability of our approach. By integrating these advanced methods, we offer a more robust framework for predicting solvation structures in calcium ion and other battery electrolytes.