Leveraging machine learning to find promising compositions for sodium-ion batteries

Sodium-containing transition-metal layered oxides are promising electrode materials for sodium-ion batteries, a potential alternative to lithium-ion batteries. However, the vast number of possible elemental compositions for their electrodes makes identifying optimal compositions challenging. In a recent study, researchers leveraged extensive experimental data and machine learning to predict the optimal composition of sodium-ion batteries. Their approach could help reduce time and resources needed during exploratory research, speeding up the transition to renewable energy.