Yiyang Li

Assistant Professor


2118 H.H. Dow
T: (734) 764-3371


BS, Electrical and Computer Engineering, Olin College of Engineering 2011
PhD, Materials Science and Engineering, Stanford University, 2016



My research interests are to develop new electrochemical materials for energy storage, brain-inspired computing, and information processing. Many ionic materials can host a high density of point defects, which are mobile and act as dopants. Whereas silicon and other semiconductors usually have dopant concentrations below 1018 cm-3, ionic materials can have dopants concentrations above 1022 cm-3, on the order of one formula unit. When integrated into an electrochemical redox system, such point defect dopants can be inserted and removed dynamically upon the application of electrochemical current and voltage. The high concentration of these point defects combined with the ability to dynamically tune the doping concentration enable, alongside other applications, a high density of energy storage and a high density of information storage. My work focuses on three mobile ions, the lithium ion, the proton, and the oxygen ion, and their interactions in a variety of host materials. For more background, please see Y. Li & W. Chueh, Electrochemical and Chemical Insertion for Energy Transformation and Switching. Ann. Rev. Mater. Res. 48, 137-65 (2018)

My first thrust is to understand Li-ion batteries by studying individual particles. Li-ion battery electrodes contain a large number of nano- and micron-sized battery particles. My goal is to understand the electrochemical and materials properties of individual battery particle building blocks, as opposed to the average property of a large number of particles, analogous to how biologists study individual cells in order to understand the behavior of an organism. 

My second thrust aims to develop new materials for low-power, memory-based computing. While machine learning and artificial neural networks have made significant impacts in a number of fields, they are extremely energy intensive due to the need to shuttle information between memory and processor. Matrix multiplications are especially energy intensive for digital processors. My research aims to develop materials for analogue non-volatile memory elements based on electrochemical redox processes, and to understand their physical mechanisms. Using analogue memory is projected to decrease systems-level energy consumption by ~ 2 orders of magnitude by conducting operations in a highly parallel manner. 

There are multiple open positions for students and postdocs; my group at UM will start September 2020. Prospective graduate students should apply to a UM department, and are welcome to contact me prior to applying. Interested postdocs should email me directly att yiyangli@umich.edu.


Truman Fellow, Sandia National Labs, 2017-20