Indian Scientists Develop AI Tool for Next-Generation Sodium-Ion Batteries
Researchers from IKST and RISE in India have created a deep-learning model to predict the operating voltage of sodium-ion battery materials. The study, published in 'Small Methods', indicates that sodium-ion batteries could offer a cost-effective alternative to lithium-ion batteries, which are expensive due to lithium scarcity. The AI model achieved a mean absolute error of 0.24 volts, significantly improving the material identification process for stable and high-voltage sodium-based cathodes.
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A team from IKST and RISE in India has developed a deep-learning tool that predicts the operating voltage of sodium-ion battery materials, potentially advancing their commercial use. While lithium-ion batteries dominate, sodium is abundant and cheaper.
The researchers trained their AI model on over 4,300 materials, achieving a mean absolute error of 0.24 volts on new compounds. The model was used to propose new cathode materials, with voltages closely matching theoretical and experimental data. The study highlights a workflow that could reduce the time and costs of identifying viable sodium-based cathodes, promoting their use in grid storage and electric mobility.
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