Han Zhong-Kang, Sarker Debalaya, Ouyang Runhai, Mazheika Aliaksei, Gao Yi, Levchenko Sergey V
Center for Energy Science and Technology, Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Moscow, Russia.
Materials Genome Institute, Shanghai University, Shanghai, P.R. China.
Nat Commun. 2021 Mar 23;12(1):1833. doi: 10.1038/s41467-021-22048-9.
Single-atom-alloy catalysts (SAACs) have recently become a frontier in catalysis research. Simultaneous optimization of reactants' facile dissociation and a balanced strength of intermediates' binding make them highly efficient catalysts for several industrially important reactions. However, discovery of new SAACs is hindered by lack of fast yet reliable prediction of catalytic properties of the large number of candidates. We address this problem by applying a compressed-sensing data-analytics approach parameterized with density-functional inputs. Besides consistently predicting efficiency of the experimentally studied SAACs, we identify more than 200 yet unreported promising candidates. Some of these candidates are more stable and efficient than the reported ones. We have also introduced a novel approach to a qualitative analysis of complex symbolic regression models based on the data-mining method subgroup discovery. Our study demonstrates the importance of data analytics for avoiding bias in catalysis design, and provides a recipe for finding best SAACs for various applications.
单原子合金催化剂（SAACs）最近已成为催化研究的前沿领域。对反应物易于解离的同时优化以及中间体结合强度的平衡，使其成为多种工业上重要反应的高效催化剂。然而，大量候选物催化性能缺乏快速且可靠的预测阻碍了新型SAACs的发现。我们通过应用一种以密度泛函输入参数化的压缩感知数据分析方法来解决这个问题。除了始终如一地预测实验研究的SAACs的效率外，我们还识别出200多种尚未报道的有前景的候选物。其中一些候选物比已报道的更稳定、更高效。我们还引入了一种基于数据挖掘方法子群发现的复杂符号回归模型定性分析新方法。我们的研究证明了数据分析对于避免催化设计中偏差的重要性，并提供了为各种应用找到最佳SAACs的方法。
