Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis

This Perspective explores the integration of machine learning potentials(MLPs)in the research of heterogeneous catalysis,focusing on their role in identifying in situ active sites and enhancing the understanding of catalytic processes.MLPs utilize extensive databases from high-throughput density functional theory(DFT)calculations to train models that predict atomic configurations,energies,and forces with near-DFT accuracy.These capabilities allow MLPs to handle significantly larger systems and extend simulation times beyond the limitations of traditional ab initio methods.Coupled with global optimization algorithms,MLPs enable systematic investigations across vast structural spaces,making substantial contributions to the modeling of catalyst surface structures under reactive conditions.The review aims to provide a broad introduction to recent advancements and practical guidance on employing MLPs and also showcases several exemplary cases of MLP-driven discoveries related to surface structure changes under reactive conditions and the nature of active sites in heterogeneous catalysis.The prevailing challenges faced by this approach are also discussed.

The Difference of Capital Input and Productivity in Service Industries: Based on Four Stages Bootstrap-DEA Model

Based on the concept of productive capital stock, this paper estimated capital input by three asset types of China’s 36 service industries in 2003–2015, and compared with the results of wealth capital stock. This study found that the wealth capital stock method underestimates the actual capital input in each sector in varying degrees, and it may interference the accuracy of productivity evaluation in sectors. According to the new estimation results of capital input, this paper further applied four stages bootstrap-DEA method to estimate industrial productivity, and calculated its confidence intervals. This study found that, the years of education and the average wage have a significant positive impact on the productivity of service industries; the productive services have a short board effect in the whole service industry.

Indirect Tax Burden of Regional Residents: Study on Long Term MRIO Model

Investigating the indirect tax burden of residents is an important way of understanding the effect of tax on income distribution and the tax burden transfer.In this paper,a long term MRIO model is constructed by using the regional input-output tables in China.We simulate the flow of valueadded tax,consumption tax and business tax in different departments of 30 provinces(except Tibet)in China's Mainland from the perspective of rows in the IO table,we obtain the real burden of three indirect taxes on urban residents,and discuss the transfer of tax burden among regions.The conclusions are as follows:There are large differences in tax burden among regions.The real tax burden shifted from the eastern region to the middle and the western regions.Indirect tax aggravates the income inequality of urban residents in different regions.The indirect tax in China has a certain degree of regression.Based on the empirical analysis,we think the government should make the following policy changes:1)Giving more consideration to the consumption terminals;2)Adjusting preferential policies of regional taxation;3)Increasing the amount of preferential policies for the western region.