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在现实潜力和溶解条件下,在动力障碍水平上选电催化剂
1Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing 211189, China.
JACS Au
|February 27, 2026
概括
这项研究建立了热力学和动力学障碍之间的定量联系,用于电催化剂选. 这加快了用于电化学降解反应 (eNRR) 的高效电催化剂的发现.
科学领域:
- 电化学 电化学 电化学
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 动力障碍对电催化剂的性能至关重要,但在计算上昂贵的屏幕.
- 目前用于选电催化剂的方法在现实条件下经常忽略动力障碍.
- 对于像电化学降解反应 (eNRR) 这样的反应,需要高效的电催化剂.
研究的目的:
- 为了建立热力学和动力学障碍之间的定量关系,用于电催化剂选.
- 开发一种更快,更准确的方法来识别高性能电催化剂.
- 应用这一策略来选单原子@硬币金属 (M1@CM) 合金用于NNRR.
主要方法:
- 使用常量潜在的初始分子动力学 (cp-AIMD) 增强采样来计算动力障碍 (ΔG#).
- 确定了热力学自由能量变化 (ΔG) 和不同硬币金属 (Cu,Ag,Au) 的动力障碍之间的线性相关性.
- 采用机器学习回归来创建 ΔG 和 ΔG# 之间的统一映射,预测误差低.
主要成果:
- 在Cu,Ag和Au的 ΔG和 ΔG#之间发现了明显的线性关系,受工作函数驱动的界面水和表面电荷的影响.
- 开发了一个统一的机器学习模型,预测来自热力学的动力障碍,误差为0.05 eV.
- 确定了一个高活性区域,并计算了Re1@Ag的全部反应路径,揭示了所有低于0.85 eV的动力障碍.
结论:
- 开发的定量关系和机器学习模型显著加快了精确的电催化剂选.
- 这一策略能够在整个反应途径中快速识别具有低动力障碍的ENRR催化剂.
- 这项研究表明了将热力学和先进的计算方法集成到电催化剂发现中的强大方法.
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