用机器学习潜力和雨集成对异质催化反应的自由能量障碍的估计
Sina Stocker1, Hyunwook Jung1, Gábor Csányi2
1Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany.
Journal of chemical theory and computation
|September 25, 2023
概括
机器学习潜力显著降低了计算成本,用于计算催化过程中的自由能量障碍. 热效应大大降低了Rh{111}上CHO分解的障碍,挑战了现有的模型.
科学领域:
- 计算化学和催化研究.
- 机器学习在预测反应动力学方面的应用.
背景情况:
- 准确预测基本反应速率常数对于建模催化过程至关重要.
- 过渡状态理论 (TST) 依赖于自由能量障碍,通常是使用密度函数理论 (DFT) 等第一原则方法计算的计算成本昂贵.
研究的目的:
- 为了证明机器学习 (ML) 原子间潜力的有效性,用于计算自由能量障碍.
- 评估热效应对CHO在Rh上的分解的影响,使用ML潜力.
主要方法:
- 开发一个自动化的代工作流程来训练ML的原子间潜力.
- 使用ML潜力进行自由能量计算,与直接DFT评估相比,提供较低的计算成本.
- 研究CHO在Rh{111}上的分解,并将热效应纳入TST.
主要成果:
- 机器学习潜能使得以显著降低的计算成本,可以进行自由能量计算.
- 对于CHO分解在Rh{111}上,观察到大量的热效应,导致自由能量屏障降低.
- 计算的自由能量障碍可以是微小的,取决于使用的DFT函数,与以前的和TST近似相比.
结论:
- ML原子间潜能为催化过程中的自由能计算提供了一个计算效率高的替代方案.
- 热效应对CHO分解屏障挑战的重大影响为合成气转化建立了微动力学模型.
- 在合成气转换中,对C2+氧化物选择性的反应机制进行重新评估是有必要的.
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