A Study of the Adsorption Properties of Individual Atoms on the Graphene Surface: Density Functional Theory Calculations Assisted by Machine Learning Techniques

被引:1
|
作者
Huang, Jingtao [1 ]
Chen, Mo [1 ]
Xue, Jingteng [1 ]
Li, Mingwei [2 ]
Cheng, Yuan [3 ]
Lai, Zhonghong [4 ]
Hu, Jin [1 ]
Zhou, Fei [5 ]
Qu, Nan [1 ]
Liu, Yong [1 ,2 ]
Zhu, Jingchuan [1 ]
机构
[1] Harbin Inst Technol, Sch Mat Sci & Engn, Harbin 150001, Peoples R China
[2] Harbin Inst Technol, Natl Key Lab Precis Hot Proc Met, Harbin 150001, Peoples R China
[3] Harbin Inst Technol, Natl Key Lab Sci & Technol Adv Composites Special, Harbin 150001, Peoples R China
[4] Harbin Inst Technol, Ctr Anal Measurement & Comp, Harbin 150001, Peoples R China
[5] Southwest Univ Sci & Technol, Sch Mat Sci & Engn, State Key Lab Environm Friendly Energy Mat, Mianyang 621010, Peoples R China
关键词
single atoms; graphene; surface adsorption; first-principles calculations; machine learning; OPTICAL-PROPERTIES; PERIODIC-TABLE; DESIGN;
D O I
10.3390/ma17061428
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
In this research, the adsorption performance of individual atoms on the surface of monolayer graphene surface was systematically investigated using machine learning methods to accelerate density functional theory. The adsorption behaviors of over thirty different atoms on the graphene surface were computationally analyzed. The adsorption energy and distance were extracted as the research targets, and the basic information of atoms (such as atomic radius, ionic radius, etc.) were used as the feature values to establish the dataset. Through feature engineering selection, the corresponding input feature values for the input-output relationship were determined. By comparing different models on the dataset using five-fold cross-validation, the mathematical model that best fits the dataset was identified. The optimal model was further fine-tuned by adjusting of the best mathematical ML model. Subsequently, we verified the accuracy of the established machine learning model. Finally, the precision of the machine learning model forecasts was verified by the method of comparing and contrasting machine learning results with density functional theory. The results suggest that elements such as Zr, Ti, Sc, and Si possess some potential in controlling the interfacial reaction of graphene/aluminum composites. By using machine learning to accelerate first-principles calculations, we have further expanded our choice of research methods and accelerated the pace of studying element-graphene interactions.
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页数:12
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