通过机器学习加速的单原子催化剂的二氧化碳还原反应的微动态建模
Qing-Meng Zhang1, Zhao-Yu Wang1, Hao Zhang1
1Department of Chemistry, School of Chemistry and Chemical Engineering, Southwest University, Chongqing, 400715, China. haozhang@swu.edu.cn.
Physical chemistry chemical physics : PCCP
|March 25, 2024
概括
研究人员选了金属--碳单原子催化剂 (SACs) 以获得高效的电化学二氧化碳减排. 机器学习加速了像Mn-NC2,Pt-NC2和Au-NC2这样有希望的催化剂的发现,用于将二氧化碳转化为有价值的碳化合物.
科学领域:
- 催化剂是一种催化剂.
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 电化学二氧化碳减排 (CO2RR) 将可再生能源转化为化学能源,减少温室气体排放.
- 金属--碳 (M-N-C) 单原子催化剂 (SAC) 对CO2RR具有前景,但识别高度活性和选择性催化剂具有挑战性.
研究的目的:
- 通过使用联合DFT和ML方法加速发现高性能M-N-C CO2RR的SAC.
- 选大量的M-N-C SAC,以获得最佳的催化活性,选择性和稳定性.
主要方法:
- 密度函数理论 (DFT) 的计算用于生成99个M-N-C SACs电化学性质的数据库.
- 机器学习 (ML) 模型将选池扩展到297个SAC,然后根据稳定性,HER抑制和CO吸附选择33个SAC.
- 基于马库斯理论的微动力模拟评估了被选中的33个SAC的活性和选择性.
主要成果:
- 该研究选了297个M-N-C SAC,确定了33个有前途的候选人.
- Mn-NC2,Pt-NC2和Au-NC2在CO2/CO转化为碳化合物方面表现出优异的催化性能.
- 这种ML辅助选方法显著降低了计算成本,并加速了催化剂探索.
结论:
- 联合DFT和ML方法为快速选CO2RR催化剂提供了有效的策略.
- 确定了Mn-NC2,Pt-NC2和Au-NC2作为从CO2中生产高能量密度碳化合物的潜在催化剂.
- 这种方法可以显著推进电化学二氧化碳减排催化剂的开发.
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