Research Interests

Yifan is an inaugural cohort of the Stanford Energy Postdoctoral Fellowship in the Department of Materials Science and Engineering and the Doerr School of Sustainability at Stanford University. Yifan earned a Ph.D. and an M.S. degree, ‘22, in Mechanical Engineering, and an M.S. degree, ‘16, in Petroleum Engineering from Stanford. Yifan earned a B.S. degree in chemical engineering from Tsinghua University. During doctoral research in computational mechanics with Prof. Wei Cai at Stanford, Yifan developed a stress-driven kinetics model of defect processes that governs the plastic deformation in metals and alloys. Yifan’s teaching and research was recognized as the Rising Star in Mechanical Engineering (2020, Berkeley University), and his doctoral dissertation won the Juan C. Simo Thesis Award from the Mechanics & Computation Group in the Department of Mechanical Engineering at Stanford. Yifan’s research interests lie in bridging the length- and time-scales for sustainable materials production and manufacturing, including developing new characterization tools and multiscale modeling and simulations.

Yifan will address the challenges in the carbon-intensive steel-making industry, which contributes to approximately 10 percent of global CO2 emissions. The need to decarbonize the steel industry has become pressing due to the increasing demand for steel in renewable energy production. Supported by Prof. Leora Dresselhaus-Marais (MatSci) and Prof. Xiaolin Zheng (MechE), Yifan will investigate hydrogen-based direct iron reduction (HyDIR), a promising solution for decarbonizing the iron-making process. This technology replaces currently widely used carbon-heavy reduction agents (cokes/coal) with green hydrogen to cut the majority (more than 75 percent) of the carbon footprint of steel-making. The main challenge of HyDIR is the scaling issue, where different physical and chemical processes at different length scales make models at each scale not transferable. This research aims to tackle this challenge by combining advanced characterization tools, data-driven statistical learning, kinetics modeling, and atomistic simulation. Specifically, Yifan’s research will focus on bridging the gap in understanding how the microstructure evolution influences the mass transport and overall kinetics of the iron-reduction. The success of this research will establish a reliable workflow for testing the HyDIR reactions, and provide a unique opportunity to reveal the fundamental connections between different length scales and the intrinsic dimensionality of the HyDIR system to guide efficient design of the iron-production process.

Yifan’s Ph.D. research combines statistical mechanics theories with high-throughput computational techniques at the atomic scale to discover defect mechanisms controlling the strength of novel engineering materials. The goal is to accurately predict the defect evolution and provide an easy-to-use cyberinfrastructure for accelerating materials design, such as refractory high-entropy alloys, ceramics, and metallic glasses. The research will benefit novel engineering materials design towards improving the efficiency of traditional and renewable energy technologies (e.g., thermal power plants and solid-state batteries) to achieve the national energy security mission while reducing environmental impact.