Integrating Experiments, Computational Approaches, and Machine Learning to Accelerate Progress in Battery Research

The demand for affordable and efficient energy storage solutions is rapidly increasing, leading to an evolution of the battery industry. However, developing from initial ideas to final products can take years or even decades. Thus, there is an urgent need to expedite technological progress, spanning from the design and mechanism understanding at the nano-scale to materials processing and production at the device level. The battery field encompasses deliverables from advanced characterizations, informative insights from theoretical modeling, and the pioneering exploration of data-driven and machine learning (ML) approaches. We believe a collaborative and interdisciplinary effort will drive innovation. Join us for an exhilarating conference of cutting-edge research in batteries, exploring the potential of integrating experimental and computational techniques, as well as the role of ML in battery research.