Smart magnesium batteries: Using AI to power greener and more reliable desalination systems

This study develops a focused AI-based optimisation framework to improve the performance of magnesium alloy batteries for renewable-powered desalination systems. The objective is to enhance voltage stability, reduce internal resistance, and extend cycle life through coordinated optimisation of material and operating parameters. An analytical–simulation methodology is adopted, combining electrochemical degradation models with machine learning prediction and genetic algorithm optimisation. Key variables include alloy composition, electrolyte type, operating temperature, and current density. Neural networks were trained using a literature-anchored dataset and validated through cross-validation, while genetic algorithms were used to identify optimal multi-objective operating conditions. The optimised Mg–Al configurations demonstrated a 25% reduction in voltage degradation, a 50% decrease in internal resistance , and a 20% increase in cycle life compared with baseline (non-optimised) conditions, achieving up to 220 stable cycles. The predictive models attained a 94.5% accuracy with a root mean square error of 0.015 V , indicating low prediction uncertainty and robust generalisation within the studied domain. These quantified improvements translate into higher energy efficiency and reduced maintenance demand for desalination applications. Overall, the results confirm that AI-assisted optimisation provides a reliable, resource-efficient pathway for designing sustainable magnesium-based energy storage systems aligned with circular economy objectives.