使用机器学习预测吸附剂在Pt(111) 和液态水界面上的化能
Jiexin Shi1,2, Xiaohong Zhang1, Venkata Rohit Punyapu2
1Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina 29634-0909, USA.
The Journal of chemical physics
|February 25, 2025
概括
机器学习模型现在预测了表面上吸附物的溶解热力学,提高了水相异质催化物的计算效率. 这些模型提供准确和快速的预测,降低成本而不会影响结果.
科学领域:
- 催化剂是一种催化剂.
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 水相异质催化对于生物质转化和电催化等工业过程至关重要.
- 精确计算溶解热力学对于催化剂性能建模至关重要.
- 目前的显式溶解方法 (DFT,MD) 是准确的,但计算密集.
研究的目的:
- 开发机器学习 (ML) 模型,用于预测Pt{111}表面上吸附物的溶解热力学.
- 在不牺牲准确性的情况下提高催化研究的计算效率.
- 为了深入了解在Pt{111}-水接口中调节溶解的因素.
主要方法:
- 开发了使用分子描述符和指纹的ML模型.
- 在已发表的水-吸附物相互作用能量,溶解能量和Pt的自由能量上训练模型{111}.
- 利用特征重要性分析来确定影响解答的关键因素.
主要成果:
- ML模型实现了高精度:0.09 eV (相互作用能量),0.04 eV (溶解能量),0.06 eV (自由溶解能量).
- 模型准确性处于传统多尺度建模的标准误差范围内.
- 确定了结,范德瓦尔斯力,溶剂密度和吸附性质作为关键因素.
结论:
- ML模型可以快速,可靠地预测Pt上的吸附物质的溶解特性.
- 这种方法显著降低了催化模型的计算成本.
- 提供了关于在水性-Pt111) 接口上的吸附盐溶解的宝贵见解.
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