Atomtransmachine: An atomic feature representation model for machine learning

被引：0
|
作者：
Hu, Mengxian
Yuan, Jianmei [1 ]
Sun, Tao
Huang, Meng
Liang, Qingyun
机构：
[1] Xiangtan Univ, Sch Math & Computat Sci, Hunan Key Lab Computat & Simulat Sci & Engn, Xiangtan 411105, Hunan, Peoples R China
来源：
COMPUTATIONAL MATERIALS SCIENCE | 2021年 / 200卷
基金：
中国国家自然科学基金;
关键词：
Atomism; Distributed representation; Feature engineering; Machine learning;
D O I：
10.1016/j.commatsci.2021.110841
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In this study, a self-monitoring model is proposed to extract the atomic characteristics of the main group elements and transition metals from several molecular structures. Different from previous studies, we use a spatial convolution layer to extract the spatial features of atoms and a multi-attention mechanism to screen their important features in forming new crystal structures. Extensive numerical analyses show that the features extracted using the proposed model are effective and can improve the efficiency of machine learning algorithms.
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收藏
页数：6
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