Leveraging machine learning to accelerate multiscale simulations for renewable energy materials

Dr. Tian Tian obtained his B.Sc. and M.Sc. in Chemistry from Tsinghua University. He completed his Ph.D. in Chemical Engineering at ETH Zürich under the supervision of Prof. Chih-Jen Shih. His doctoral research focused on the multiscale simulation and engineering of the interfacial properties of two-dimensional materials. From 2021 to 2023, he received the Swiss National Science Foundation (SNSF) Postdoc Mobility Fellowship to conduct postdoctoral research at Carnegie Mellon University with Prof. Zachary W. Ulissi. He worked on machine-learning-assisted material simulations, particularly the fine-tuning of pretrained graph neural network models for computational catalysis and developing machine-learning-assisted computational workflows. Before joining UofA, he briefly held a postdoctoral position at Georgia Institute of Technology under the supervision of Prof. Phanish Suryanarayana and Prof. Andrew J. Medford, developing software communication layers for the machine-learning-enabled density functional theory (DFT) package.

Dr. Tian’s research group at UofA and Amii will focus on machine-learning-accelerated simulations for interfacial material design, exploring applications in two-dimensional materials, energy storage, light-emitting materials, and colloidal soft materials. His research aims to tackle the challenges of vast configuration spaces in interfacial problems, leveraging machine learning to accelerate multiscale material simulations. Additionally, he is committed to bridging computational and experimental approaches by developing open-source simulation tools and machine-learning-driven design frameworks for optimizing material properties and synthesis techniques.