机器学习的高通量选稀土SACs与不同的协调环境为HER的HER
Meiling Liu1, Qiming Fu1, Wei Zhong1
1Faculty of Materials Metallurgy and Chemistry, Jiangxi University of Science and Technology, Ganzhou 341000, People's Republic of China. liuchao198967@126.com.
概括
机器学习 (ML) 选了稀土单原子催化剂 (SACs) 的进化反应 (HER) 性能. 使用ML和DFT计算,确定了三种表现出色的有前途的催化剂.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 电化学 电化学 电化学
背景情况:
- 开发用于演化反应 (HER) 的高效电催化剂对于清洁能源技术至关重要.
- 稀土单原子催化剂 (SAC) 为催化应用提供独特的电子特性.
- 需要高通量选方法来加快新型SAC的发现.
研究的目的:
- 为 HER 执行稀土 SAC 的高通量选.
- 使用机器学习 (ML) 和密度函数理论 (DFT) 评估催化性能.
- 为了识别具有高HER活性的潜在SAC.
主要方法:
- 使用ML用于稀土SAC的高通量选.
- 使用DFT计算100个催化剂组的吸附的吉布斯自由能量 (ΔG*H).
- 训练了一种渐变增强回归器 (GBR) 模型来预测她的表现.
主要成果:
- 在预测 ΔG*H方面,GBR 模型实现了高精度 (R2 = 0.970,RMSE = 0.157).
- 鉴定出了三种具有优异 HER 性能的稀土 SAC,用 ΔG*H的能量 < 0.20 eV 表示.
- 证明了ML引导选用于催化剂发现的有效性.
结论:
- 结合DFT的ML是一种强大的方法,可以加速发现高效的HER催化剂.
- 已识别的稀土SAC显示出在生产中实际应用的巨大潜力.
- 这项研究为设计下一代电催化剂提供了一条途径.
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