从化学反应中发现二氧化碳吸附和脱离的途径 暂时动力学光谱学神经网络建模的化学反应神经网络建模
Jay Shukla1, Xiaohui Qu2, Zubin Darbari1
1Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, New York 11794, United States.
The journal of physical chemistry letters
|April 1, 2025
概括
我们结合了红外光谱学和机器学习来理解表面反应. 我们的数据驱动方法揭示了表面二氧化碳吸附的详细机制.
科学领域:
- 表面科学是一门科学.
- 化学动力学 化学动力学
- 计算化学是一种计算化学.
背景情况:
- 从实验数据中解释复杂的表面反应机制是具有挑战性的.
- 短暂的动力数据提供了丰富的机械信息,但需要先进的分析工具.
- 了解金属表面上的二氧化碳吸附/溶解对于催化是至关重要的.
研究的目的:
- 开发和验证一个数据驱动的框架来解释表面反应机制.
- 将时间分辨率红外光谱与化学反应神经网络 (CRNNs) 结合起来.
- 通过使用短暂的动力学数据,阐明Pd{111}上的CO吸附和脱附机制.
主要方法:
- 利用时间分辨率的气体脉冲红外光谱来收集短暂的动力数据.
- 采用化学反应神经网络 (CRNNs) 来建模和分析实验数据.
- 系统地评估各种反应机制,包括不同的吸附部位 (空洞,桥梁).
主要成果:
- 具有明显吸附点动态的模型显示与实验吸收率数据相似.
- 对光谱峰稳定性和预测动态的分析确定了一个首选的机制.
- 在物理上最一致的机制涉及到桥梁站点上的初级CO吸附,然后转换为空洞站点.
结论:
- 使用CRNNs的数据驱动方法可以从有限的实验数据中提取详细的表面反应机制.
- 机器学习有效地弥合了短暂的动力测量和分子层面的理解之间的差距.
- 这个框架为表面科学和催化学中的机械解释提供了一个强大的工具.
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