Scientific Data Mining in Nanoscale Visualization of Reaction Pathways in Li-ion batteries
Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory
Abstract: In the study of hierarchically complex and chemically heterogeneous systems, e.g. the researches in energy materials, it is critical to understand the relationship between the macroscopic material property and the microscopic morphology/chemistry. This is because the overall material behaviors are believed to be the ramifications of the interplays of its structural and chemical properties at fine length scales. While the capability of visualization of the chemical heterogeneity at nanoscale has been demonstrated using synchrotron based spectro-microscopy techniques, it remains challenging to efficiently and robustly extract the scientifically relevant information from the big data.
In this presentation, I will review the state-of-the-art developments in both the synchrotron based spectro-microscopy and the associated big data mining methods. Case studies in the field of battery material studies [1-5] will be presented to demonstrate the strength of data mining approach that is capable of extracting information from the imaging data and providing key insights into the degradation mechanisms of the battery electrode at particle level.
Biography: Dr. Yijin Liu received his B.S. and Ph.D. in Optics from the University of Science and Technology of China in 2004 and 2009, respectively. He was a postdoctoral scholar at the Stanford Synchrotron Radiation Lightsource before he became an associate staff scientist in 2012 and, later, a staff scientist in 2015 at the SLAC National Accelerator Laboratory. His research interest is in the X-ray based multi-length-scale spectroscopic imaging techniques with scientific focus in the energy material study and the related scientific big data mining.