Application Of Molecular Simulation Method Based On Machine Learning In Synthesis And Electrocatalytic Of High-entropy Alloys

The global energy shortage is becoming more and more seriously,people are committed to find safe,efficient,clean and green energy.Electrocatalysts play a significant role in the field of energy conversion.Therefore,the reasonable design of electrocatalysts is the research target of electrocatalysis.The method of experimental spectroscopy and the First-principles calculations provide a powerful supporting for understanding and designing of electrocatalysts.At present,the calculation method will gradually play an increasingly important role.The early First-principles method,molecular simulation method and finite element simulation method,and the recent First-principles molecular dynamics method,all of them have brought many new understandings and ideas to the electrocatalytic system.With the development of computer science and the rise of machine learning methods,the development space of material simulation and material computing have been increased.In the field of energy science,High-entropy alloys as a new electrocatalyst have aroused researchers’wide concern due to their excellent activity in various catalytic reactions.However,there are few studies of the entropy effect on the surface of catalysts.Therefore,the theoretical mechanism of high-entropy alloys can improve the electrocatalysis activity is still ambiguous.In this paper,two works have been carried out:The theoretical mechanism analysis of the high activity of High-entropy alloys as the electrocatalyst.Meanwhile,taking electrocatalytic glycerol oxidation（GOR）as an example,explaining the theoretical methods and the simulation methods based on machine learning of application in electrocatalysis.the simulation of HEA used in the oxidation of glycerol.The full paper is divided into two parts:（1）From a theoretical point of view,we have quantitatively analyzed the principle of High-entropy alloys can enhance the activity of electrocatalysis progress through‘cocktail effect’and‘breaking the scaling relationship effect’,which are based on the high entropy characteristics of materials.Firstly,we assumed the adsorption should obey the normal distribution,and defined the concept of‘effective adsorption’,which is the adsorption energy of the sites that have the highest contribution to the apparent turnover efficiency.High-entropy alloys due to site diversity will format‘cocktail effect’,which can move the effective adsorption energy to the volcanic peak,and the HEA will always closer to the volcanic top.For the‘breaking the scaling relationship effect’,sites migration can occur on the surface of HEA,which will break the scaling relationship of traditional catalysis and improve electrocatalysis activity.Meanwhile,we invented a simple theoretical model,it relates the variance of the key intermediate’s adsorption energy and the catalytic activity of the materials.In short,the larger the variance is,the reaction activity of materials will be better.（2）The machine learning method combined with the molecular dynamics method have been used to learn the accurate force field of the High-entropy alloys.After that,Monte Carlo simulations were imported to achieve elemental distribution of these nanoparticles.And then,the associated HEA nanoparticles were used for density functional theory（DFT）calculation of GOR.According to a theoretical volcano of GOR with adsorption Gibbs energies of C₃H₆O₃(ΔG_C3H6O3)as the‘activity descriptor’,we found that HEA have better reaction activity.Meanwhile,based on the calculation results and the site effect and variance model that mentioned in the first work,the Mo sites on the surface of HEA is the most active sites,and when the coordination elements are Mo,Ni,and Mn,the electrocatalyst will have more better reaction activity.Based on the above research work,we discussed the theoretical mechanism of the high catalytic activity of HEA based on‘cocktail effect’and‘breaking the scaling relationship effect’.At the same time,we combined with the machine learning methods and the First-principles methods,the High-entropy alloys nanoparticles have been simulated and used for DFT calculation.The measures of optimizing and improving the electrocatalysis activity of the materials have been explored,too.The results of this paper can provide guidance and ideas for the material analysis and material designing in the field of energy science.