Development of solid-state polymer electrolytes and application of artificial intelligence in Li batteries
Date: 2021/04/27 - 2021/04/27
Academic Seminar: Development of solid-state polymer electrolytes and application of artificial intelligence in Li batteries
Speaker: Dr. Ying Wang, from Lawrence Berkeley National Lab (California, USA)
Time: 9:00 a.m.-10:00 a.m., April 27, 2021 (Beijing Time)
Location: via Zoom
Development of solid-state electrolytes is a critical strategy to address safety concerns in high-energy-density Li-metal batteries. In state-of-art solid-state electrolytes, there remain critical challenges, i.e., inhomogeneous deposition of metal and high interfacial resistance between electrode and electrolyte. We solved these practical issues comprehensively based on fabricating a series of solid-state polymer composite electrolytes through a double-helical polymeric liquid crystal combined with an ionic liquid and concentrated Li salt. This high strength (200 MPa) and non-flammable solid electrolyte possesses outstanding conductivity (3 mS·cm-1 at 25 °C) and electrochemical stability (5.6 V vs Li|Li+) while suppressing dendrite growth and exhibiting low interfacial resistance (32 Ω·cm2) and overpotentials (≤ 120 mV @ 1 mA·cm-2) during Li symmetric cell cycling. We also demonstrated an ultra-fast lithium-ion conduction mechanism in the liquid crystal grain boundaries of the composite electrolytes. In the future, development of a database cluster related to energy materials will assist fabrication of advanced solid-state polymer electrolytes. Through cutting edge technologies in artificial intelligence, i.e., deep learning and generative adversarial network, it will be more efficient to unravel the structure and property relationship in resultant materials. Meanwhile, combine with in-depth investigation of the backend mechanisms, including ion conduction, interfacial transport, nano-confinement and phase transition, this systematic research plan will incubate new technologies and directions in the field of energy storage materials.
Dr. Ying Wang received PhD degree in Macromolecular Science and Engineering from Virginia Tech (Virginia, USA) in 2016. Meanwhile, she obtained M.Sc. degree in Statistics from the same university. Dr. Wang is engaged in conducting research on development and characterization of advanced solid-state polymer electrolytes for energy storage and conversion devices. After graduation, she started postdoctoral research work at Lawrence Berkeley National Lab (California, USA), where she worked on the collaborative project between US Department of Energy and 24M technology. With about one year postdoc training, she became a Data Scientist at Hughes Network System (Maryland, USA), which is No. 1 Satellite network provider in US. With theorical and practical experience in both academia and industry, she continuously followed and developed the cutting-edge technologies and ideas in both material science and artificial intelligence (AI). Dr. Wang has published a series of articles as the first author in journals, including Nature Materials, Nature Communications, Advanced Materials, and Macromolecules. She has also done a lot of work in the field of big data and machine learning. In recent years, Dr. Wang has applied for three related U.S. patents based on application of AI in data analysis and strategy making. The comprehensive background of Dr. Wang ensures better integration of diverse research disciplines in AI as well as material science and also shows promise in creating more meaningful insights in material science.