卷积神经网络
梯度升压
计算机科学
Boosting(机器学习)
人工智能
深度学习
决策树
超导电性
特征提取
模式识别(心理学)
特征(语言学)
交替决策树
机器学习
物理
凝聚态物理
决策树学习
随机森林
增量决策树
语言学
哲学
作者
Yabo Dan,Rongzhi Dong,Zhuo Cao,Xiang Li,Chengcheng Niu,Shaobo Li,Jianjun Hu
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2020-01-01
卷期号:8: 57868-57878
被引量:17
标识
DOI:10.1109/access.2020.2981874
摘要
Superconductors have been one of the most intriguing materials since they were discovered more than a century ago. However, superconductors at room temperature have yet to be discovered. On the other hand, machine learning and especially deep learning has been increasingly used in material properties prediction and discovery in recent years. In this paper, we propose to combine the deep convolutional neural network (CNN) model with fully convolutional layers for feature extraction with gradient boosting decision tree (GBDT) for superconductors critical temperature (Tc) prediction. Our prediction model only uses the elemental property statistics of the materials as original input and learns a hierarchical representation of superconductors using convolutional layers. Computational experiments showed that our convolutional gradient boosting decision tree (ConvGBDT) model achieved the state-of-the-art results on three superconductor data sets: DataS, DataH, and DataK. By visually comparing the raw elemental feature distribution and the learned feature distribution, it is found that the convolutional layers of our ConvGBDT can learn features that can more effectively distinguish cuprate and iron-based superconductors. On the other hand, the GBDT part of our ConvGBDT model can learn the sophisticated mapping relationship between extracted features and the critical temperatures to obtain good prediction performance.
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