Xu Jiayan, Cao Xiao-Ming, Hu P
Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, School of Chemistry and Molecular Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China.
Phys Chem Chem Phys. 2021 May 19;23(19):11155-11179. doi: 10.1039/d1cp01349a.
Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.
多相催化在现代化学工业中发挥着重要作用。对于新型催化剂的合理设计而言，理解表面上的反应是最为关键的方面。典型的工业催化过程，如合成气转化和甲烷利用，会产生一个包含数千种中间体和反应对的庞大反应网络。这种复杂性不仅源于物种之间转化的排列组合，还源于不同表面位点提供的额外反应通道。尽管在原子尺度上研究表面反应取得了成功，但从头算方法巨大的计算成本阻碍了对如此复杂反应网络的探索。随着催化研究的不断增多，机器学习作为一种新兴工具，可以利用积累的反应数据来模拟从头算方法的输出，以实现快速的反应预测。在此，我们简要总结反应预测的传统工作流程，包括反应网络生成、从头算热力学和微观动力学建模。还介绍了机器学习中常用回归模型的概述。作为全从头算计算的一种有前景的替代方法，机器学习原子间势受到了关注。此外，我们考察了这些方法在加速反应预测、探索反应网络和计算催化剂设计方面的辅助应用。最后，我们展望了在计算研究反应以及在多相催化中实现机器学习算法的未来方向。
