arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.07531cond-mat.mtrl-sci
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Accelerating inverse crystal structure prediction by machine learning: a case study of carbon allotropes

Wen Tong, Qun Wei, Haiyan Yan, Meiguang Zhang, Xuanmin Zhu

英文摘要:

Based on structure prediction method, the machine learning method is used instead of the density function 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 an 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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