The Application Of Convolutional Neural Networks In Density Functional Computation For Structural Prediction

Nowadays,with the increase of the application of new energy materials,the density functional computing and other large-scale material computing also face new challenges.Convolutional neural network is a feedforward artificial neural network,which not only uses less preprocessing,but also has the characteristics of independent from prior knowledge and artificial work in the characteristic design,and has become one of the hotspots in many scientific fields.Based on convolutional neural network and density functional calculation,the crystal structure of materials is predicted and analyzed in this paper.The model of convolutional neural network can be trained dynamically and automatically without manual assembly of training data set.Therefore,it is not necessary to significantly adjust the prediction algorithm of crystal structure,and the crystal structure prediction based on density functional calculation can be carried out.Moreover,the convolutional neural network can be combined with DFT to verify each other and improve the efficiency and accuracy of structure prediction in material calculation.In order to follow up the continuous development of new energy materials and the wide application of machine learning algorithm,this paper explores the combination of machine learning prediction method and density functional computing pretreatment to carry out the calculation and analysis of material structure and crystal structure prediction application.Based on density functional theory,the correlation algorithm analysis of convolutional neural network for crystal structure prediction of materials is studied.Firstly,the theory of density functional correlation is summarized based on the recent development of material computing technology,and the application of machine learning to crystal structure prediction of materials is discussed.Then the convolutional neural network is built based on the TensorFlow software environment,and the corresponding network model is analyzed through the structure prediction training.The crystal structures of 13 different ids of sodium elements were predicted,which provided the crystal structure pretreatment support for the computational simulation of related materials.