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Abstract:Lithium-ion battery (LIB) is featured by structural and chemical complexities across a broad range of length and time scales. The studies of battery operation, degradation, and failure mechanisms require a thorough and systematic investigation from the structural, chemical, mechanical, and dynamic perspectives. Understanding and interpreting the big data generated by state-of-the-art experimentation in this research field need to leverage the novel computing developments.

In this talk, I will review my groups research activities in this field over the past few years. I will discuss the macro-to-nano hierarchy of a lithium battery cell. We utilize a suite of state-of-the-art X-ray techniques and develop data mining methods to harvest valuable information from the big data. We look into the morphological and structural defects and their electrochemical consequences from the electrode-level down to the atomic-scale. We demonstrate the effectiveness of our approach for understanding the detrimental effects, which, in turn, informs the next-generation battery material design. Finally, I will provide my perspective for the future developments in this field.

Bio:Yijin Liu received his PhD degree in Optics through a joint education program at University of Science & Technology of China (USTC, Hefei, China) and Institute of High Energy Physics (IHEP, Beijing, China). He is Lead Scientistat the SLAC National Accelerator Laboratory, and leads the Transmission X-ray Microscopy (TXM) program at Stanford Synchrotron Radiation Lightsource (SSRL).In addition to his scientific research activities, Liu is the Founder & CEO of Xpertography, Inc., a recently formed startup company.

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Data-guided, Multi-scale, and High-dimensional Understanding of the Battery Degradation - Argonne National Laboratory