海歸學者發起的公益學術平臺
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交流學術，偶爾風月
納米糰簇表面析氫反應的催化活性取決於氫吸附位點的結構，預測團簇的催化活性需要對各種可能的吸附結構開展模擬，計算量龐大。採用基於描述符的機器學習可以顯著降低相關預測的工作量。
來自芬蘭阿爾託大學的Adam Foster教授等分析了目前最先進的結構描述符，即原子位置平滑重疊（SOAP）、多體張量表示（MBTR）和原子中心對稱函數（ACSF）用於機器學習納米糰簇表面氫吸附自由能的可靠性。他們以2D的MoS2和AuxCuy合金作為測試體系，掃描了納米糰簇表面氫吸附的勢能面，比較了不同描述符用於核嶺回歸的預測性能。通過對91個MoS 2納米糰簇和24個AuxCuy納米糰簇組成的數據集的分析表明，SOAP相比其他描述符具有顯著優越的預測性能，可以作為吸附能預測的候選工具。
該文近期發表於npjComputational Materials 4:3 (2018)，英文標題與摘要如下，點擊左下角「閱讀原文」可以自由獲取論文PDF。
Machine learning hydrogen adsorption on nanoclusters through structural descriptors
Marc O. J. Jäger, Eiaki V. Morooka, Filippo Federici Canova, Lauri Himanen & Adam S. Foster
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.
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