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小分子吸附能量预测用于电催化剂的高通量选

Srishyam Raghavan¹, Brian P Chaplin¹, Shafigh Mehraeen¹

¹Department of Chemical Engineering, University of Illinois at Chicago, 929 West Taylor Street, Chicago, Illinois 60607, United States.

Journal of chemical information and modeling

|August 25, 2023

概括

机器学习模型,水晶图卷积神经网络 (CGCNN) 和原子线图神经网络 (ALIGNN),准确地预测了电催化剂上的吸附能量. 为了更快的电催化剂设计,ALIGNN在CGCNN上表现出优越的性能.

科学领域:

计算化学是一种计算化学.
材料科学是一种材料科学.
机器学习机器学习

背景情况:

对吸附能量的准确预测对于设计用于电化学反应的高效电催化剂至关重要.
使用几何和电子描述符的传统方法在预测金属氧化物和非金属等不同基板上的吸附方面存在局限性.
计算密集的电子结构计算阻碍了潜在电催化剂的快速选.

研究的目的:

评估机器学习算法,特别是CGCNN和ALIGNN在预测各种电催化剂上的小分子吸附能量的性能.
将这些机器学习模型的准确性与传统的几何和电子描述器进行比较.
评估CGCNN和ALIGNN在预测氧进化反应超电位方面的能力.

主要方法:

密度函数理论 (DFT) 的计算被用来获得参考吸附能量.
使用了两个机器学习模型,即晶体图卷积神经网络 (CGCNN) 和原子线图神经网络 (ALIGNN).
通过比较预测的吸附能和超潜与DFT结果,使用平均绝对误差 (MAE) 来评估性能.

主要成果:

在预测吸附能量的方面,CGCNN和ALIGNN显著超过了传统的几何和电子描述器.
ALIGNN始终显示出比CGCNN更高的准确性,平均绝对误差改进范围从0.02到1.0 eV.
两种模型都在预测氧演变反应超电位方面表现出强的表现,CGCNN的MAE为0.06V,ALIGNN的MAE为0.05V.

结论:

机器学习模型,特别是ALIGNN,为预测电催化剂的吸附能提供了一种计算效率高,准确的方法.
这些模型可以通过克服传统描述器和DFT计算的局限性来加速新型电催化剂的发现和开发.
开发的机器学习框架显示了预测关键电化学反应参数的前景,有助于催化剂设计.

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