(1. School of Mechanical Engineering, Guizhou University, Guiyang 550025, China) (2. Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China) (3. Department of Computer Science and Engineering, University of South Carolina, Columbia SC 29208，USA)
As a new research mode in material science, material informatics has attracted wide attention. With the rapid increase of material data, machine learning methods are more and more used in the analysis of material data to obtain instructive physical and chemical laws from a large number of material data. This paper focuses on the convolutional neural network, using data from more than 4000 materials collected from the Material Project database to predict formation energy of materials, and the prediction results are accurate. Then, the gradient of feature map is analyzed, we observe that there are some certain correlations between gradient and material properties, and under the guidance of gradient matrix, the possible distribution of feature map with target properties can be found. Finally, the patterns recognized by the convolutional neural network are analyzed, which further verifies that the convolutional neural network can achieve excellent prediction results of material property.