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

作　　者：Wen Tong Qun Wei Hai-Yan Yan Mei-Guang Zhang Xuan-Min Zhu 

机构地区：[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

出　　处：《Frontiers of physics》2020年第6期97-103,共7页物理学前沿（英文版）

基　　金：This work was financlally supported by the Fundamental Research Funds for the Central Universities,the Na-tional Natural Science Foundation of China(Grant Nos.11965005 and 11964026);the 111 Project(No.B17035);the Natural Sci-ence Basie Research plan in Shaanxi Province of China(Grant Nos.2020JM-186 and 2020JM-621).

摘　　要：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.

关 键 词：machine learning crystal structure prediction CARBON 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]