基于合金中的结构和电子特征工程,准确和高效的机器学习模型用于预测演变反应催化剂的结构和电子特征工程
Jingzi Zhang1,2, Yuelin Wang1,2, Xuyan Zhou1,2
1School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China. linxi@hit.edu.cn.
Nanoscale
|June 19, 2023
概括
机器学习模型准确地预测了通过水电解生产的高性能合金电催化剂. 这加速了用于进化反应 (HER) 的新材料的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 电化学 电化学 电化学
- 计算化学的计算化学
背景情况:
- 通过水电解来生产的高性能合金电催化剂的设计是一个重大挑战.
- 庞大的数量可能的合金组成使得实验和计算选难.
研究的目的:
- 开发精确高效的机器学习 (ML) 模型,用于合金电催化剂的预测材料设计.
- 确定用于进化反应 (HER) 的有希望的合金候选者.
主要方法:
- 将合金的电子和结构性质纳入ML模型.
- 使用光梯度增强 (LGB) 算法进行预测.
- 预测吸附的吉布斯自由能量 (ΔGH*) 的估计特征重要性.
主要成果:
- 使用LGB模型实现了高的确定系数 (R2 = 0.921) 和低的平方根平均误差 (RMSE = 0.224 eV).
- 确定了电子和结构吸附点特征,这些特征对于预测 ΔGH*至关重要.
- 从2290个候选物中选了84个具有ΔGH*的潜在合金.
结论:
- 结合结构和电子特征的ML模型可以加速发现高效的电催化剂.
- 这种方法为开发HER和其他异质反应的催化剂提供了新的见解.
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