揭示了电子和语音激发在激光诱导的氧气激活中的相互作用在Ru(0001) 上
Xiangrui Wang1, Jiamin Wang1, Paul Spiering2
1Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA.
The Journal of chemical physics
|September 17, 2025
概括
机器学习加速了对金属表面激光诱导氧气激活的模拟. 电子激发驱动这个过程,声子在能量消耗中起到次要作用.
科学领域:
- 表面科学是一门学科.
- 计算化学计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 金属表面的激光诱导动力学涉及复杂的电子和音声相互作用.
- 模拟这些过程需要先进的计算方法来捕捉不同的时间尺度.
研究的目的:
- 开发一种机器学习加速分子动力学方法,用于研究激光诱导的表面动力学.
- 调查异构电子摩擦在Ru上的氧激活中的作用.
主要方法:
- 利用机器学习加速分子动力学模拟.
- 纳入模拟框架中的异型电子摩擦.
- 在Ru上分析了激光诱导的氧激活的非adiabatic动态.
主要成果:
- 在前800 fs内,电子激发主导氧气激活.
- 声子作为消散通道,缓冲能量损失并重新分配动能.
- 一个非线性收益率-流动关系 (Y ~ Fn) 突出了电子刺激的意义.
- 确定了z方向作为氧气扩散的主要模式.
结论:
- 开发的ML加速方法扩展了非adiabatic动态的模拟能力.
- 电子激发是Ru上的激光诱导氧气激活的主要驱动因素.
- 该研究提供了对金属表面能量转移机制和激活途径的洞察.
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