摘要
在功能材料应用中,带隙往往起着重要的作用,如光电材料一般为宽带隙半导体,而热电材料为窄带隙半导体,因此对指定类别的材料体系带隙进行快速而准确的预测对于功能材料应用具有非常重要的科学意义.然而,通过基于第一性原理的高通量计算获取高精度带隙的方法耗时长,效率低,而实验上系统测量大量材料体系带隙也不现实,所以基于统计学的机器学习预测方法就成了一种有前景的可能性替代方案.本文设计了一种集成学习模型用于有效而准确地预测带隙值.在已计算过带隙值的热电材料类金刚石化合物的基础上,一方面利用单组元组分替换策略产生大批量相似化合物,并用查重技术过滤掉重复体系,得到356个相似材料体系.另一方面结合机器学习技术,构建高效的带隙预测模型,预测并验证了50个相似材料体系的带隙值.通过实验证明,该预测模型具有77.73%的准确率,且足够健壮稳定,可以广泛应用于需要进行大批量带隙预测的热电材料的研究情景中.
The bandgap often plays an important role in functional materials applications.For example,optoelectronic materials are generally wide bandgap semiconductors,while thermoelectric materials are narrow bandgap semiconductor materials.Therefore,predicting the bandgap rapidly and accurately for a given class of materials structures has great scientific importance for the functional materials applications.However,considering that the method of obtaining high-precision band gaps based on first-principles high-throughput calculations is time consuming and inefficient,and it is also not realistic to systematically measure a large number of material system band gaps.Machine learning methods based the statistics may be a promising alternative.This paper designs an ensemble learning model for effectively and accurately predicting bandgap values.Based on the calculated band gap values of diamond-like structures in thermoelectric materials,on the one hand,single component substitution strategy was used to generate large quantities of similar compounds,and the repetitive structures was filtered out by using the structural repeatability examination technique,resulting in 356 unique material structures.On the other hand,in combination with machine learning techniques,an efficient band gap prediction model was constructed,and by which the band gap values of 50 similar material systems are predicted and verified.As is the result of the experiment,this prediction model has 77.73%accuracy.It is enough robustness and stability to be widely used in thermoelectric materials application scenarios which require large band gap prediction.
作者
徐永林
王香蒙
李鑫
席丽丽
倪剑樾
朱文浩
张武
杨炯
XU YongLin;WANG XiangMeng;LI Xin;XI LiLi;NI JianYue;ZHU WenHao;ZHANG Wu;YANG Jiong(School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China;Materials Genome Institute of Shanghai University, Shanghai 200444. China)
出处
《中国科学：技术科学》
EI
CSCD
北大核心
2019年第1期44-54,共11页
Scientia Sinica(Technologica)
基金
国家重点研发计划(编号:2017YFB0701501)
国家自然科学基金重大研究计划重点项目(编号:91630206)资助
关键词
类金刚石结构
带隙
组分替换
机器学习
集成学习
diamond-like structures
bandgap
component substitution approach
machine learning
ensemble learning
