Catalytic activity of the hydrogen evolution reaction on nanoclusters depends on diverse adsorption site structures. Machine learning reduces the cost for modelling those sites with the aid of descriptors. We analysed the performance of state-of-the-art structural descriptors Smooth Overlap of Atomic Positions, Many-Body Tensor Representation and Atom-Centered Symmetry Functions while predicting the hydrogen adsorption (free) energy on the surface of nanoclusters. The 2D-material molybdenum disulphide and the alloy copper–gold functioned as test systems. Potential energy scans of hydrogen on the cluster surfaces were conducted to compare the accuracy of the descriptors in kernel ridge regression. By having recourse to data sets of 91 molybdenum disulphide clusters and 24 copper–gold clusters, we found that the mean absolute error could be reduced by machine learning on different clusters simultaneously rather than separately. The adsorption energy was explained by the local descriptor Smooth Overlap of Atomic Positions, combining it with the global descriptor Many-Body Tensor Representation did not improve the overall accuracy. We concluded that fitting of potential energy surfaces could be reduced significantly by merging data from different nanoclusters.

氢逸出反应对纳米团簇的催化活性取决于不同的吸附位点结构。机器学习借助描述符减少了对这些站点进行建模的成本。我们分析了最先进的结构描述符的性能，同时预测了纳米团簇表面的氢吸附（自由）能，该结构描述符具有原子位置的平滑重叠，多体张量表示和原子中心对称功能。二维材料二硫化钼和铜金合金用作测试系统。进行了簇表面上氢的势能扫描，以比较核垄回归中描述子的准确性。通过求助于91个二硫化钼簇和24个铜金簇的数据集，我们发现，平均绝对误差可以通过同时（而不是分别）在不同集群上进行机器学习来降低。吸附能通过局部描述符原子位置的平滑重叠来解释，将其与全局描述符多体张量表示相结合并不能提高整体精度。我们得出的结论是，通过合并来自不同纳米团簇的数据，可以显着减少势能面的拟合。