| Name | Nguyen Xuan Thi |
|---|---|
| Affiliation | Graduate School Engineering Department of Applied Chemistry |
| Title of the Research | Searching for New Proton-Conducting Oxide: A Physics Informed Machine Learning Approach |
| Outline of Research | Protonic ceramic fuel cells (PCFCs) have huge potential to overcome the shortcomings of both PEFCs and SOFCs, greatly expanding the scope and influence of hydrogen energy. My research will focus on improving the performance of proton conducting oxides, the bottleneck for this technology, by predicting new materials. The novelty in my approach will be in addressing previous issues in this field in two ways: I will use theoretical modeling to find descriptors that are directly related to proton diffusivity; my predictions will be based on calculations on local structures. I will be using a machine learning model with evolutionary algorithms to explore ~106 oxides. This will greatly improve the theoretical understanding of proton conductivity in oxides, and aid in the discovery of novel materials for PCFCs in a short time frame. |