Accelerating Calculation Of Configurational Energy Of Zr-O Binary System Based On Machine Learning And Cluster Expansion Method

At present,the exploration of new materials is often based on continuous attempts to seek better performance under the guidance of experience,and there is a few of contingency.First-principles calculation is still the most mainstream method for calculating the inherent properties of materials.However,the realization of this calculation method requires complicated preparation work and consumes a lot of calculation time,and even has higher requirements for the professionalism of calculation software operations.In recent years,machine learning algorithms are increasingly being applied to the field of exploring new materials and calculating material properties due to their excellent computing efficiency and predictive capabilities for processing large amounts of data,gradually showing their revolutionary advantages and winning great attention of the industry insiders.Therefore,first-principles calculations based on machine learning algorithms to accelerate the inherent properties of materials have very important research significance.Aiming at the calculation of the formation energies of the Zr-O binary system under different configurations,this paper first selects machine learning features based on the cluster expansion method,and uses appropriate cluster functions to distinguish different atomic order degrees,that is,different configurations can be described by different cluster functions,and cluster correlation functions are used as fingerprints to distinguish clusters with different symmetry orbitals.Using this method,a synthetic cluster with multi-body interactions with extended Hamiltonian was obtained,and the formation energy of the crystal in different configurations was calculated according to the first principles.In addition,based on the cluster expansion method,this paper ignores the linear constraint between the cluster correlation function and the configuration energy,and establishes a nonlinear mapping relationship between the cluster correlation function and the configuration energy by a machine learning model.The cluster correlation function is used as the input feature of regression algorithm models such as neural network and Gaussian process regression,and the configurational energy prediction is carried out through the data set training model given by the first principles,and the configurational energy calculation is accelerated.This paper is based on four machine learning algorithms combining cluster correlation functions and gap state sets for model training and testing.Among them,four optimization schemes including genetic algorithm combined with cluster expansion formula,recursive feature elimination combined with support vector regression and random forest are used to optimize the features of cluster correlation functions.Furthermore,the prediction results of all accelerated the configurational energy calculation schemes are compared and analyzed.The final analysis results show that the cluster expansion formula is used as the genetic algorithm to evaluate the optimization characteristics of the model,and combined with the Gaussian process regression model to predict the optimal acceleration configuration energy calculation scheme.