Artificial Intelligence Assisted Design and Synthesis of High Entropy Materials for Electrochemical Catalysis
(Jie Liu, and Stephano Curtarolo- 3 weeks)
The key objective of the project is to demonstrate the use of high entropy materials (HEMs) as catalysts with optimized properties beyond that can be achieved by traditional catalysts. Due to the unique nature of high entropy materials, they offer a surface atomic structure that cannot be obtained previously. HEMs are stable single-phase materials with more than 5 different metal elements that are stabilized by their high entropy. These materials represent a new direction in materials research and can be the solution for some of the long-standing problems in applications like catalysis due to the high temperature stability of the surface structure that contains multiple metal sites in their surfaces. By combining rapid synthesis and AI based materials design, we can explore the use of HEMs as catalysts in electrochemistry. Our hypothesis is that the precise design of new class of HEMs with the optimized surface biding sites for specific chemical reactions can significantly enhance the catalytic efficiency. The use of HEMs in catalysis can impact a broad class of chemical reactions.
1. Students will understand and be able to discuss the concept of catalysis;
2. Students will be able to describe the relationship between computational results and experimental performance of materials.