机器学习辅助的描述符 (CO和OH) 关于基于Cu的双金属合金的结合能的近似估计
Pallavi Dandekar1, Aditya Singh Ambesh1, Tuhin Suvra Khan2
1MMEC Lab, Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Kandi, Sangareddy-502285, Telangana, India. shelaka@che.iith.ac.in.
机器学习模型,特别是极端梯度增强回归器 (xGBR),可以通过预测CO*和OH*的结合能来快速选催化剂材料. 这大大降低了与传统方法相比的计算成本,用于诸如酸分解等应用.
科学领域:
- 计算材料科学 计算材料科学
- 催化剂是一种催化剂.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 催化剂材料的高通量选在计算上是昂贵的.
- 结合能是催化剂性能的一个关键描述因素.
- 机器学习 (ML) 为降低成本提供了一个潜在的解决方案.
研究的目的:
- 评估八种ML模型,用于预测Cu3M合金表面上的CO*和OH*结合能.
- 确定用于催化剂选的最有效的ML模型.
- 与DFT计算相比,评估ML模型的计算效率.
主要方法:
- 利用了线性,内核和基于树的集体ML模型.
- 采用周期表金属属性作为特征.
- 在111终端Cu3M合金表面上训练和测试模型.
- 根据密度函数理论 (DFT) 的计算,验证了ML预测.
主要成果:
- 极端梯度增强回归器 (xGBR) 展示了卓越的性能.
- xGBR实现了0.091 eV (CO) 和0.196 eV (OH) 的根平均平方误差 (RMSEs).
- xGBR产生了高的R2分数,分别为0.970 (CO) 和0.890 (OH).
- ML的预测比DFT计算快得多,MAE为0.02-0.03 eV.
结论:
- xGBR是一种高效的ML模型,用于预测催化剂选中的结合能.
- 基于ML的选加速了有效的A3B型双金属合金的识别.
- 预测的结合能可以与初始微动力学建模 (MKM) 集成,以进一步优化.
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