用Pt1/TiO2对CO氧化形成催化循环的反应中间体进行了配置采样研究
Nicholas Humphrey1, Selin Bac1, Shaama Mallikarjun Sharada1,2
1Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089, USA.
The Journal of chemical physics
|September 20, 2024
概括
我们使用机器学习在白金催化剂上发现了CO氧化反应的新反应途径. 这揭示了关键的中间配置,显著改变了估计的反应速度.
科学领域:
- 不同质的催化剂.
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
背景情况:
- 原子分散催化剂,如Pt1/TiO2,对于CO氧化等化学反应至关重要.
- 了解反应中间体和通路对于优化催化剂性能至关重要.
- 传统的计算方法可能难以探索催化中间体的全部配置空间.
研究的目的:
- 开发一种自动化方法来识别CO氧化反应中间体的可能配置.
- 调查周围的局部协调环境在二氧化碳结合中的作用.
- 改进对Pt1/TiO2系统的催化转换频率的估计.
主要方法:
- 结合初始分子动力学 (AIMD) 模拟与无监督机器学习.
- 使用t分布式随机邻居嵌入 (t-SNE) 来减少AIMD快照的维度.
- 使用基于噪声的应用程序的基于层次密度的空间聚类 (HDBSCAN) 来基于白金的局部协调来分组配置.
主要成果:
- 确定了与各种CO2结合配置相对应的以前未发现的局部最小值.
- 揭示了CO2可以以多种方式与原子和TiO2支协调.
- 这些新的最小值引入了额外的基本步骤,导致营业额的频率估计与以前的计算不同几个数量级.
结论:
- 使用机器学习进行自动化配置采样对于对催化循环进行准确的计算研究至关重要.
- 详细探索中介结构显著影响了CO氧化预测的动力学.
- 这种方法提供了对原子分散催化剂反应机制的更全面的理解.
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