Deep Learning Model for Prediction of Diffusion in Defect Substances

Abstract

Actual diffusion activity function is an important metric utilized to describe the diffusion activities of a vacancy defect substance. In this paper, we propose a deep learning three-dimensional convolutional CNN model (D3-CNN). A 3D convolution has its kernel slides in three dimensions as opposed to two dimensions with 2D convolutions. 3D convolution is more suitable for three-dimensional data. We also propose an amplification learning technique to predict the actual diffusion activity of a vacancy defect substance, which is impacted by the geometrical parameters of the defect substance and the vacancy distribution function. In this model, the geometric parameters of a three-dimensional constructed vacancy defect substance are generated. The 3D dataset is obtained by the atoms diffusion defect (ADD) simulation model. The geometric parameters of the 3D vacancy defect substance are computed by the proposed amplification technique. The 3D geometric parameters and the diffusion activity values are applied to a deep learning model for training. The actual diffusion activity values of a substance with a vacancy size ranging from size 0.52 mm to 0.61 mm are used for training, and the actual diffusion activity values of substance vacancy of size between 0.41 and 1.01 are classified by the three-dimensional network. The model can realize high speed and accuracy for the actual diffusion activity value. The mean relative absolute errors between the D3-CNN and the ADD models are 0.028–7.85% with a vacancy size of 0.41 to 0.81. For a usual sample with a vacancy of size equal to 0.6, the CPU computation load required by our model is 14.2 × 10−2 h, while the time required is 15.16 h for the ADD model. These results indicate that our proposed deep learning model has a strong learning capability and can function as an influential model to classify the diffusion activity of compound vacancy defect substances.