材料科学
体积模量
碳纤维
剪切模量
晶体结构预测
弹性模量
模数
晶体结构
复合材料
结晶学
化学
复合数
作者
Wen Tong,Qun Wei,Haiyan Yan,Meiguang Zhang,Xuanmin Zhu
标识
DOI:10.1007/s11467-020-0970-8
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.
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