Haili Jia, PhD candidate in the Department of Chemical and Biomolecular Engineering, was recently recognized by the American Physical Society (APS) Topical Group on Data Science (GDS) with an IMPACT Award. Jia, a member of Paulette Clancy’s lab and visiting scholar at Argonne National Laboratory, received the award for research on data science and machine learning as applied to advanced structure characterization. She will present her work at the APS March meeting in Las Vegas.
Jia’s project seeks to improve the performance of NMC-type lithium ion battery materials by modifying the material structure via an automated loop which doesn’t rely on human-influenced machine learning. This automation can predict the battery’s structures and properties without the need to manually input parameters or manually calibrate simulated results with experimental measurements. The automation can also identify data points require a manual adjustment. She is also working in collaboration with Chao Wang’s lab on a machine learning approach to enable spectral imaging analysis for complex nanomaterial systems. This research was recently published in American Chemical Society Nano.
“I started my PhD with traditional simulation and modeling,” Jia said. “I took machine learning and deep learning classes during my second year at Hopkins and was mesmerized at how data science helps us glean extraordinary insights into the way things work. I became especially fascinated with integrating data science with classic computational approach in material characterization and development, and I am determined to take it up as the ultimate goal for my PhD, or even a lifelong pursuit.”