Researchers at the the University of Surrey have developed a library of 28 materials to help identify better candidates for building sodium-ion batteries.
The repository could help find alternative materials to replace the conventional lithium-ion batteries that are made from scarce resources.
”We rely heavily on lithium-ion batteries, but lithium resources are relatively scarce and unevenly distributed around the world,” said Daniel Commandeur, lecturer at the School of Chemistry and Chemical Engineering and corresponding author of the study.
He noted that sodium-ion batteries are emerging as a robust alternative; however, it need further analysis to improve their performance over repeated charging cycles and the amount of energy they can hold.
”Sodium is far more abundant, making sodium-ion batteries an important technology to develop,” Commandeur added.
Library to access performance
The team explored how modifying the chemical composition of sodium manganese oxide could improve its performance as a cathode. This key battery component determines how much energy a battery can store and how efficiently it operates.
During the investigation, they created a library composed of 28 different metal compositions and compared their performance in sodium-ion batteries. The collection was made by replacing the manganese with varying amounts of different metals, including iron, titanium, and vanadium.
Iron boosted energy-storage capacity, vanadium improved cycling stability, and small amounts of titanium enhanced high-rate performance. Some titanium-containing materials retained over 90% of their capacity after 100 charge-discharge cycles, the study found.
AI to pace the hunt
The team agreed that a major hurdle is finding a material that delivers the optimum performance.
To help explore suitable alternative materials, researchers developed an automated approach to analyze the data generated by the experiments. They trained the machine-learning models with datasets generated in their experiments.
”This work is a first step toward building the larger data sets needed for machine learning to guide that search—helping us identify promising materials faster and accelerate the development of better sodium-ion batteries,” said Commandeur.
Researchers said expanding the dataset could eventually let AI predict promising battery compositions before they are produced and tested in the lab. However, the current library remains too small for reliable predictions.
Expanding the approach to a wider range of cathode materials could generate the large, high-quality datasets needed for machine learning to accelerate the discovery of new battery materials. In time, this could help scientists design sodium-ion cathodes optimized for specific needs, such as higher energy storage, faster charging, or longer-lasting performance.
“What stood out most was how relatively small changes in composition could shift the balance between how much energy a material can store, how it performs at higher rates, and how well it lasts. Mapping those relationships gives us a much clearer picture of where to look next,” said Eden Marks, a Ph.D. student in space engineering and co-lead author of the study.
The findings were published in a new study in the Journal of Materials Chemistry.