通过密度函数理论和机器学习,合理设计用于降解反应的双原子δ5-烯催化剂
Dewei Zhang1, Shengluo Ma1, Yunwen Wu2
1China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 201306, China.
The Journal of chemical physics
|June 25, 2025
概括
在δ5-烯上的双原子催化剂显示出通过电催化还原来可持续生产氨的希望. 机器学习准确地预测了催化剂的性能,强调了N2结合是有效合成氨的关键.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 催化剂是一种催化剂.
背景情况:
- 可持续的氨 (NH3) 生产至关重要.
- 电催化降解反应 (NRR) 提供了一个绿色的替代方案.
- 高效的电催化剂对于NRR至关重要.
研究的目的:
- 对NRR进行研究,对δ5烯的双原子催化剂 (DAC) 进行研究.
- 选并识别高性能NRR电催化剂.
- 集成DFT和ML用于催化剂设计.
主要方法:
- 密度函数理论 (DFT) 对反应路径的计算.
- 机器学习 (ML) 模型 (XGBR,GBR) 与SHAP分析.
- 对23个同核和21个异核DAC进行选.
主要成果:
- TiTi@δ5-BP和ZrZr@δ5-BP显示出较高的NRR活性 (UL = -0.60 V).
- 在异质核候选物中,NbHf@δ5-BP表现出最低的限制电位 (UL = -0.48 V).
- ML的预测与DFT结果有很好的一致性.
结论:
- DFT和ML集成加速了高效NRR电催化剂的设计.
- 原子之间的结合相互作用对N2吸附至关重要.
- 这项研究提供了对DAC用于可持续氨合成的见解.
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