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Accelerating inverse crystal structure prediction by machine learning: A case study of carbon allotropes |
Wen Tong1, Qun Wei1( ), Hai-Yan Yan2, Mei-Guang Zhang3(), Xuan-Min Zhu4 |
1. School of Physics and Optoelectronic Engineering, Xidian University, Xi’an 710071, China
2. College of Chemistry and Chemical Engineering, Baoji University of Arts and Sciences, Baoji 721013, China
3. College of Physics and Optoelectronic Technology, Baoji University of Arts and Sciences, Baoji 721016, China
4. School of Information, Guizhou University of Finance and Economics, Guiyang 550025, China |
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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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| Keywords
machine learning
crystal structure prediction
carbon
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Corresponding Author(s):
Qun Wei,Mei-Guang Zhang
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Issue Date: 21 July 2020
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