About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
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| Symposium
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Energy Technology 2027: Advancement in Energy Materials - Theory, Simulation, Characterization and Application
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| Presentation Title |
Machine Learning Assisted Computational Design of High Entropy Catalysts for Renewable Energy Technology |
| Author(s) |
Guofeng Wang |
| On-Site Speaker (Planned) |
Guofeng Wang |
| Abstract Scope |
To accelerate development of energy materials for renewable energy technology, we have performed machine learning assisted atomistic simulations to predict the complex composition-structure-property relation of high entropy catalysts. (1) We investigated the formation of solid solution phase in Co0.12Ni0.14Ru0.43Rh0.30 alloy nanoparticles through a combined molecular dynamics and Monte Carlo approach. Our simulation results indicated that the local severe lattice distortion could block the diffusion of atoms and hence lead to a stable solid solution phase during a carbothermal shock synthesis procedure. Such high entropy alloy nanoparticles were demonstrated to be highly efficient and durable for catalyzing thermal decomposition of NH3 for H2 production. (2) The first-principles calculations and machine learning model were employed to understand how the multi-elemental mixing improves the nitrate reduction reaction activity to form NH3 on Cu-Ru-Pd-Pt high entropy nanoparticles. It was predicted that Cu-Ru-Pd-Pt catalyst would exhibit a higher nitrate reduction activity than unary Ru catalyst.
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| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Energy Conversion and Storage, High-Entropy Alloys |