Prediction of band gap of transition metal sulfide with Janus structure by deep learning atomic feature representation method

With the development of artificial intelligence, Machine Learning (ML) is more and more widely used in material computing. To apply ML to the prediction of material properties, the first thing to achieve is to obtain effective material feature representation. In this paper, an atomic feature representation method is used to study a low dimensional, densely distributed atomic eigenvector and applied it to the band gap prediction in material design. According to the type and number of atoms in the material chemical formula, the Transformer Encoder is used as the model structure, and a large number of material chemical formula data are trained to extract the features of the training elements. Through the clustering analysis of the atomic feature vectors of the main group elements, it can be found that the element features can be used to distinguish the element categories. The Principal Component Analysis of the atomic eigenvectors of the main group elements shows that the projection of the atomic eigenvectors on the first principal component reflects the outermost electron number corresponding to the element. This illustrates the effectiveness of atomic eigenvectors extracted using the Transformer models. Subsequently, the atomic feature representation method was used to represent the material characteristics, using three ML methods: Random Forest (RF), Kernel Ridge Regression (KRR) and Support Vector Regression (SVR), to predict the two-dimensional band gap of the transition metal chalcogenide compound MXY (M represents transition metal, X and Y are different chalcogenide elements) with Janus structure. The hyperparameters of ML model are determined by parameter searching. To obtain stable results, the ML model is tested by 5-fold cross-validation. The results obtained from the three ML models show that the average absolute error of the prediction of atomic feature vectors based on deep learning is smaller than that obtained from the traditional Magpie method and the Atom2Vec method. For the atomic eigenvector method proposed in this paper, the prediction accuracy of the KRR model is the best compared with the Magpie and Atom2Vec methods. This also shows that the atomic feature vector proposed in this paper has a certain correlation between the features, and is a low-dimensional and densely distributed feature vector. Visual analysis and numerical experiments of material property prediction show that the atomic feature representation method based on deep learning extraction proposed in this paper can effectively characterize material features and can be applied to the task of material band gap prediction.