粒子马尔科夫链蒙特卡洛方法推断在过渡的表面动力学
Marija Iloska1, J Anibal Boscoboinik2, Qin Wu2
1Department of Electrical & Computer Engineering, Stony Brook University, Stony Brook, New York 11794, United States.
Journal of chemical theory and computation
|January 2, 2025
概括
我们开发了一种新的贝叶斯方法来分析一氧化碳 (CO) 在表面的吸附和脱附. 这种方法提高了化学过程的模型解释性和不确定性量化.
科学领域:
- 物理化学 物理化学
- 表面科学是一门学科.
- 计算化学的计算化学
背景情况:
- 一氧化碳 (CO) 在 (Pd) 表面的吸附和脱附在催化和化学过程中至关重要.
- 时间分辨率红外光谱学提供了详细的运动数据,需要先进的分析方法.
- 可解释的模型和不确定性量化对于理解复杂的化学动态至关重要.
研究的目的:
- 开发一种新的贝叶斯方法,用于研究Pd上的CO吸附/脱附.
- 学习关键参数:时间依赖的覆盖范围,速率常数,激活能量和预指数因子.
- 在化学过程模型中整合物理约束并量化不确定性.
主要方法:
- 一个概率模型被设计为吸附-脱附系统.
- 采用粒子马尔科夫链蒙特卡洛 (PMCMC) 采样方法推断隐藏的覆盖范围和速率常数.
- 用两个贝叶斯式公式来确定激活能量和预指数因子.
主要成果:
- 贝叶斯方法成功地学习了表征CO吸附和脱附动力学的参数.
- 推断的激活能量和预指数因子与现有的实验文献一致.
- 该方法在结合物理约束和量化参数不确定性方面表现出灵活性.
结论:
- 新的贝叶斯框架为分析表面吸附/脱附过程提供了一个强大的方法.
- 该方法为化学动力学提供了改进的模型解释性和不确定性量化.
- 该方法适用于其他化学系统和光谱数据.
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