Data-Driven Discovery and Understanding of Ultrahigh-Modulus Crystals

doi:10.1021/acs.chemmater.0c04146

	Data-Driven Discovery and Understanding of Ultrahigh-Modulus Crystals
	Shao, Qian 1; Li, Ruishan 1; Yue, Zuogong 2; Wang, Yanlei 3,4; Gao, Enlai 1
刊名	CHEMISTRY OF MATERIALS
	2021-02-23
卷号	33 期号:4 页码:1276-1284
ISSN号	0897-4756
DOI	10.1021/acs.chemmater.0c04146
英文摘要	High-modulus materials that yield small elastic deformation under mechanical loads hold great promise for use in a wide range of engineering applications. However, the discovery and understanding of high-modulus materials remain a long-term challenge, as the traditional experimental trial-and-error approach is time-consuming. In this work, we discovered two new ultrahigh-modulus crystals (CN2 and OsN2), exhibiting a maximum Young's modulus (1555.3 and 1382.7 GPa, respectively) greater than that of diamond (1152.0 GPa in our calculations), by data mining of 13 122 crystals and first-principles verifications. More surprisingly, the density of CN2 is lower than that of diamond, which endows it with high modulus and light weight. Furthermore, we explored the mechanical behaviors of the discovered ultrahigh-modulus crystals by performing tensile tests and found that CN2 and OsN2 also boast high strength while maintaining decent ductility. The underlying mechanism for the ultrahigh modulus of these two crystals was explained by analyses of the electron density and bond order. To further broaden our understanding, a data-driven analysis was conducted to quantify the structure-modulus correlations of 10 903 crystals, and six crucial structural and compositional features that are highly correlated to the maximum Young's moduli of crystals were identified. Based on these six features, a nonlinear classifier was developed, which successfully predicted crystals possessing a maximum Young's modulus greater than 1000 GPa and separated them from the others, making this approach useful for falsifiable prediction and discovery of high-modulus crystals. Based on this understanding, suggestions were made to guide the design and synthesis of high-modulus crystals. Additionally, the formation and stabilities of CN2 and OsN2 were explored for practical applications.
资助项目	National Natural Science Foundation of China[11902225] ; National Natural Science Foundation of China[11702199] ; Natural Science Foundation of Hubei Province[2019CFB174]
WOS研究方向	Chemistry ; Materials Science
语种	英语
出版者	AMER CHEMICAL SOC
WOS记录号	WOS:000623043600019
资助机构	National Natural Science Foundation of China ; Natural Science Foundation of Hubei Province
内容类型	期刊论文
源URL	[http://ir.ipe.ac.cn/handle/122111/47800]
专题	中国科学院过程工程研究所
通讯作者	Gao, Enlai
作者单位	1.Wuhan Univ, Sch Civil Engn, Dept Engn Mech, Wuhan 430072, Hubei, Peoples R China 2.Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia 3.Chinese Acad Sci, Inst Proc Engn, Beijing Key Lab Ion Liquids Clean Proc, CAS Key Lab Green Proc & Engn, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Innovat Acad Green Manufacture, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Shao, Qian,Li, Ruishan,Yue, Zuogong,et al. Data-Driven Discovery and Understanding of Ultrahigh-Modulus Crystals[J]. CHEMISTRY OF MATERIALS,2021,33(4):1276-1284.
APA	Shao, Qian,Li, Ruishan,Yue, Zuogong,Wang, Yanlei,&Gao, Enlai.(2021).Data-Driven Discovery and Understanding of Ultrahigh-Modulus Crystals.CHEMISTRY OF MATERIALS,33(4),1276-1284.
MLA	Shao, Qian,et al."Data-Driven Discovery and Understanding of Ultrahigh-Modulus Crystals".CHEMISTRY OF MATERIALS 33.4(2021):1276-1284.