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The increasing demand for sustainable energy solutions necessitates advancements in energy storage technologies. This project focuses on accelerating the discovery of high-performance electrode materials for sodium-ion batteries (SIBs) through AI-driven material screening. With lithium-ion batteries (LIBs) facing supply constraints and environmental concerns, SIBs offer a viable alternative due to sodium’s abundance and low cost. However, SIBs lag in energy density and performance. By employing AI models to rapidly screen material databases, this project aims to predict and optimize key properties such as capacity, volume change, and voltage. The integration of machine learning and materials science will expedite the identification of promising materials, ultimately improving the performance of next-generation sodium-ion batteries for widespread applications.
Problem Statement, Challenges and Potential Benefits
s the world transitions to renewable energy, the need for scalable, efficient, and cost-effective energy storage systems becomes critical. While lithium-ion batteries (LIBs) have been the industry standard for applications such as electric vehicles and grid storage, their reliance on scarce and expensive lithium resources poses significant challenges. Sodium-ion batteries (SIBs) have gained attention as a more sustainable and affordable alternative, but their lower energy density and cycling stability limit their use in high-performance applications.
Current methods for developing new battery materials are labor-intensive and costly, involving lengthy trial-and-error processes. There is a clear need for a systematic approach to predict and optimize the properties of new electrode materials for SIBs. Artificial intelligence (AI) presents a unique opportunity to transform the discovery process by using machine learning models to rapidly screen large datasets, predict material properties, and identify the best candidates for high-performance SIBs.
This project aims to address the key challenges of SIB development—enhancing energy density, reducing volume change during cycling, and improving voltage stability—by applying AI to material discovery. The project will create predictive models for screening large material databases, identifying promising electrode candidates, and optimizing them for performance. By leveraging AI, this research seeks to significantly accelerate the time-to-market for next-generation sodium-ion batteries.
Desirable Outcomes and Deliverables
• Creation of a robust AI model for predicting key performance metrics of sodium-ion battery materials, including capacity, volume change, and voltage.
• Development of a comprehensive database of predicted materials, allowing for rapid screening and optimization of electrode materials for SIBs.
• Experimental validation of top-performing materials identified through AI-driven screening, providing practical insights into their electrochemical properties.
• Dissemination of results through peer-reviewed publications and conference presentations, contributing to the field of energy storage and AI-driven material discovery.
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Formatting to follow IEEE, use published papers.