(Invited) Data-Driven Discovery: Accelerating Lithium-Ion Battery Innovation Through High-Throughput Experimentation and AI-Powered Predictive Modeling
TL;DR
Wildcat’s data-driven approach to battery innovation is shared, the capabilities of the high-throughput system are highlighted, and key insights from deploying machine learning models to transform Lithium-ion battery research are discussed.
Abstract
Lithium-ion batteries are among the most complex energy storage technologies, with performance driven by intricate interactions across diverse materials and operating conditions. Despite advances in modeling, predicting battery behavior from individual components remains a challenge, making physical experimentation essential. To accelerate discovery, Wildcat has developed a high-throughput research platform that generates high-quality, reproducible, and cross-comparable data; more importantly, including both successful and unsuccessful results. This comprehensive dataset provides a strong foundation for predictive modeling. Building on this, we are integrating artificial intelligence to uncover patterns, optimize designs, and enhance research efficiency. In this presentation, we will share Wildcat’s data-driven approach to battery innovation, highlight the capabilities of our high-throughput system, and discuss key insights from deploying machine learning models to transform Lithium-ion battery research.