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A Study of the Adsorption Properties of Individual Atoms on the Graphene Surface: Density Functional Theory Calculations Assisted by Machine Learning Techniques.
Huang, Jingtao; Chen, Mo; Xue, Jingteng; Li, Mingwei; Cheng, Yuan; Lai, Zhonghong; Hu, Jin; Zhou, Fei; Qu, Nan; Liu, Yong; Zhu, Jingchuan.
Affiliation
  • Huang J; School of Materials Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
  • Chen M; School of Materials Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
  • Xue J; School of Materials Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
  • Li M; National Key Laboratory for Precision Hot Processing of Metals, Harbin Institute of Technology, Harbin 150001, China.
  • Cheng Y; National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Harbin Institute of Technology, Harbin 150001, China.
  • Lai Z; Center for Analysis, Measurement and Computing, Harbin Institute of Technology, Harbin 150001, China.
  • Hu J; School of Materials Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
  • Zhou F; State Key Laboratory for Environment-Friendly Energy Materials, School of Materials Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, China.
  • Qu N; School of Materials Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
  • Liu Y; School of Materials Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
  • Zhu J; National Key Laboratory for Precision Hot Processing of Metals, Harbin Institute of Technology, Harbin 150001, China.
Materials (Basel) ; 17(6)2024 Mar 20.
Article in En | MEDLINE | ID: mdl-38541582
ABSTRACT
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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Materials (Basel) Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Materials (Basel) Year: 2024 Document type: Article