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Accelerating inverse crystal structure prediction by machine learning: A case study of carbon allotropes

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Abstract

Based on structure prediction method, the machine learning method is used instead of the density functional theory (DFT) method to predict the material properties, thereby accelerating the material search process. In this paper, we established a data set of carbon materials by high-throughput calculation with available carbon structures obtained from the Samara Carbon Allotrope Database. We then trained a machine learning (ML) model that specifically predicts the elastic modulus (bulk modulus, shear modulus, and the Young’s modulus) and confirmed that the accuracy is better than that of AFLOW-ML in predicting the elastic modulus of a carbon allotrope. We further combined our ML model with the CALYPSO code to search for new carbon structures with a high Young’s modulus. A new carbon allotrope not included in the Samara Carbon Allotrope Database, named Cmcm-C24, which exhibits a hardness greater than 80 GPa, was firstly revealed. The Cmcm-C24 phase was identified as a semiconductor with a direct bandgap. The structural stability, elastic modulus, and electronic properties of the new carbon allotrope were systematically studied, and the obtained results demonstrate the feasibility of ML methods accelerating the material search process.

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Acknowledgements

This work was financially supported by the Fundamental Research Funds for the Central Universities, the National Natural Science Foundation of China (Grant Nos. 11965005 and 11964026), the 111 Project (No. B17035), and the Natural Science Basic Research plan in Shaanxi Province of China (Grant Nos. 2020JM-186 and 2020JM-621). All authors thank the computing facilities at High Performance Computing Center of Xidian University.

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Authors and Affiliations

  1. School of Physics and Optoelectronic Engineering, Xidian University, Xi’an, 710071, China

    Wen Tong & Qun Wei

  2. College of Chemistry and Chemical Engineering, Baoji University of Arts and Sciences, Baoji, 721013, China

    Hai-Yan Yan

  3. College of Physics and Optoelectronic Technology, Baoji University of Arts and Sciences, Baoji, 721016, China

    Mei-Guang Zhang

  4. School of Information, Guizhou University of Finance and Economics, Guiyang, 550025, China

    Xuan-Min Zhu

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  1. Wen Tong
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  4. Mei-Guang Zhang
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Correspondence to Qun Wei or Mei-Guang Zhang.

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Tong, W., Wei, Q., Yan, HY. et al. Accelerating inverse crystal structure prediction by machine learning: A case study of carbon allotropes. Front. Phys. 15, 63501 (2020). https://doi.org/10.1007/s11467-020-0970-8

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