飞行中的动力蒙特卡洛模拟与神经网络潜力进行表面扩散和反应
Tomoko Yokaichiya1, Tatsushi Ikeda1, Koki Muraoka1
1Department of Chemical System Engineering, The University of Tokyo, Tokyo 113-8656, Japan.
The Journal of chemical physics
|May 24, 2024
概括
我们开发了一个自适应的动力蒙特卡洛模拟方案,使用神经网络潜力准确地模拟表面反应中的吸附物相互作用. 这种方法突出了侧面相互作用在催化中的关键作用.
科学领域:
- 计算化学和材料科学计算化学和材料科学
- 表面科学和催化剂.
- 化学动力学 化学动力学
背景情况:
- 动力蒙特卡洛 (KMC) 模拟对于模拟表面过程至关重要.
- 精确计算吸附和激活能量对于KMC的准确性至关重要.
- 亚酸盐-亚酸盐相互作用显著影响表面反应动态.
研究的目的:
- 开发一个适应性的KMC方案,将神经网络潜力 (NNP) 纳入用于飞行中的能量计算.
- 研究吸附剂-吸附剂侧面相互作用对表面扩散和反应的影响.
- 为了证明该方案对现实的催化系统的适用性.
主要方法:
- 实施适应性KMC框架,利用NNP进行能源评估.
- 随时计算吸附和激活能,同时考虑吸附物效应.
- 存储和重复使用计算的配置和能量,以提高仿真效率.
- 适用于Pd{111}/Pt{111}上的吸附/扩散和Pt{111}上的CO氧化.
主要成果:
- 适应性NNP-KMC方案成功模拟了表面过程.
- 模拟显示了亚酸盐-亚酸盐侧面相互作用的显著影响.
- 模拟与没有侧面相互作用的模拟之间的比较量化了它们的影响.
- 实现了表面扩散和反应通路的准确预测.
结论:
- 开发的自适应NNP-KMC方案为研究异质催化提供了强大的工具.
- 亚酸盐-亚酸盐侧面相互作用对于理解表面扩散和反应机制至关重要.
- 该方案显示了各种催化反应建模的广泛潜力.
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