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Volume 16
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10.3390/ma16072633
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Article

Accelerating Density Functional Calculation of Adatom Adsorption on Graphene via Machine Learning

by
Nan Qu
1,†,
Mo Chen
1,†,
Mingqing Liao
1,
Yuan Cheng
2,
Zhonghong Lai
3,
Fei Zhou
1,
Jingchuan Zhu
1,*,
Yong Liu
1,* and
Lin Zhang
4
1
School of Materials Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
2
National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Harbin Institute of Technology, Harbin 150001, China
3
Center of Analysis, Measurement and Computing, Harbin Institute of Technology, Harbin 150001, China
4
Biological Physics, Department of Physics and Astronomy, University of Manchester, Oxford Road, Manchester M13 9PL, UK
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Materials 2023, 16(7), 2633; https://doi.org/10.3390/ma16072633
Submission received: 2 March 2023 / Revised: 20 March 2023 / Accepted: 22 March 2023 / Published: 26 March 2023
(This article belongs to the Special Issue Study on Advanced Metal Matrix Composites)
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Abstract

Graphene has attracted significant interest due to its unique properties. Herein, we built an adsorption structure selection workflow based on a density functional theory (DFT) calculation and machine learning to provide a guide for the interfacial properties of graphene. There are two main parts in our workflow. One main part is a DFT calculation routine to generate a dataset automatically. This part includes adatom random selection, modeling adsorption structures automatically, and a calculation of adsorption properties. It provides the dataset for the second main part in our workflow, which is a machine learning model. The inputs are atomic characteristics selected by feature engineering, and the network features are optimized by a genetic algorithm. The mean percentage error of our model was below 35%. Our routine is a general DFT calculation accelerating routine, which could be applied to many other problems. An attempt on graphene/magnesium composites design was carried out. Our predicting results match well with the interfacial properties calculated by DFT. This indicated that our routine presents an option for quick-design graphene-reinforced metal matrix composites.
Keywords: graphene; adsorption; machine learning; DFT calculation graphene; adsorption; machine learning; DFT calculation

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MDPI and ACS Style

Qu, N.; Chen, M.; Liao, M.; Cheng, Y.; Lai, Z.; Zhou, F.; Zhu, J.; Liu, Y.; Zhang, L. Accelerating Density Functional Calculation of Adatom Adsorption on Graphene via Machine Learning. Materials 2023, 16, 2633. https://doi.org/10.3390/ma16072633

AMA Style

Qu N, Chen M, Liao M, Cheng Y, Lai Z, Zhou F, Zhu J, Liu Y, Zhang L. Accelerating Density Functional Calculation of Adatom Adsorption on Graphene via Machine Learning. Materials. 2023; 16(7):2633. https://doi.org/10.3390/ma16072633

Chicago/Turabian Style

Qu, Nan, Mo Chen, Mingqing Liao, Yuan Cheng, Zhonghong Lai, Fei Zhou, Jingchuan Zhu, Yong Liu, and Lin Zhang. 2023. "Accelerating Density Functional Calculation of Adatom Adsorption on Graphene via Machine Learning" Materials 16, no. 7: 2633. https://doi.org/10.3390/ma16072633

APA Style

Qu, N., Chen, M., Liao, M., Cheng, Y., Lai, Z., Zhou, F., Zhu, J., Liu, Y., & Zhang, L. (2023). Accelerating Density Functional Calculation of Adatom Adsorption on Graphene via Machine Learning. Materials, 16(7), 2633. https://doi.org/10.3390/ma16072633

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