了解Ru上的二氧化碳的光诱导脱氧和氧化情况 (000001) 使用神经网络潜在能量表面
Ivan Žugec1, Auguste Tetenoire2, Alberto S Muzas3,1
1Centro de Física de Materiales CFM/MPC (CSIC-UPV/EHU), Paseo Manuel de Lardizabal 5, 20018 Donostia-San Sebastián, Spain.
JACS Au
|May 31, 2024
概括
这项研究引入了一种用于金属表面超快光诱导动态的新计算方法,提高了对吸附物像二氧化碳脱附等吸附物行为的研究的准确性和降低了研究成本.
科学领域:
- 表面科学是一门科学.
- 计算化学是一种计算化学.
- 超快的动力学 超快的动力学
背景情况:
- 研究金属表面上吸附物的超快光诱导动力学是至关重要的.
- 像 (Te, Tl) -AIMDEF这样的现有方法在计算上昂贵,限制了实验性比较.
- 了解激光激发的电子和格子动态是关键.
研究的目的:
- 开发一种计算效率高的方法来建模超快的光诱导动力学.
- 在Ru上构建一个准确的潜在能量表面 (PES) 对CO和O的共吸收.
- 为了使理论模型和实验脱吸概率之间的定量比较.
主要方法:
- 使用嵌入式原子神经网络方法创建一个PES.
- 使用ab initio分子动力学与电子摩擦和恒温器 (Te, Tl) -AIMDEF进行短时间动力学.
- 模拟长期轨迹的统计学意义.
主要成果:
- 开发的 PES 准确地复制了短期的 ab initio 动态.
- 该方法在长轨迹上产生了统计学上显著的数据,与实验有很好的相关性.
- 在激光诱导条件下,发现了一种新的动态捕获状态,即CO脱吸的前体.
结论:
- 该理论框架已得到验证,用于描述光诱导脱附和CO氧化.
- 结果支持不平衡的热电子和声子在这些过程中的作用.
- 新的PES方法为研究复杂的表面动态提供了一个计算上可行的途径.
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