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ZnSnO3

弹性数据 ELA

MP-ID: mp-13334 · 空间群: R3c (#161)

基础信息

化学式
ZnSnO3
MP-ID
mp-13334
元素数
3
位点数
10
密度
6.5568
g/cm³
体积
117.57
ų
晶胞参数 a
5.6591
Å
晶胞参数 b
5.6591
Å
晶胞参数 c
5.6591
Å
α 角
56.43
°
β 角
56.43
°
γ 角
56.43
°
空间群
R3c
点群
3m
晶体系统
Trigonal
布拉维格子
Trigonal R
稳定性
否
堆积分数
0.7400

X 射线衍射

衍射图谱
横轴为 2θ (°),纵轴为相对强度,CuKa , λ=1.5406 Å
衍射峰(Top 24)
2θd (Å)Ihkl
22.91 3.8826 11.5 (1,1,0)
22.91 3.8826 23.0 (1,0,1)
31.72 2.8212 100.0 (2,1,1)
33.49 2.6755 83.1 (1,0,-1)
37.95 2.3707 3.3 (2,2,2)
38.64 2.3301 6.4 (2,1,0)
40.97 2.2031 4.9 (2,0,0)
46.80 1.9413 40.6 (2,2,0)
51.50 1.7744 33.4 (3,2,1)
53.91 1.7007 6.0 (2,1,-1)
55.34 1.6601 13.8 (3,3,2)
58.77 1.5713 38.4 (3,1,0)
59.88 1.5447 28.5 (2,-1,-1)
66.26 1.4106 7.6 (4,2,2)
69.07 1.3598 10.5 (4,3,3)
70.38 1.3377 10.7 (2,0,-2)
73.12 1.2942 4.0 (4,1,1)
75.11 1.2648 3.4 (3,0,-1)
76.32 1.2478 10.4 (4,3,1)
78.98 1.2122 6.4 (4,4,2)
79.26 1.2087 4.8 (3,2,-1)
79.26 1.2087 9.6 (3,-1,2)
82.86 1.1651 2.7 (4,2,0)
82.86 1.1651 5.4 (4,0,2)
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能量属性

形成能/原子
-1.93943
eV/atom
能量/原子
-5.96206
eV/atom
凸包距离
0.0414
eV/atom
未修正能量
-5.5499
eV/atom
体积/原子
11.76
ų
c/a 比
1.0000

电子属性

金属性
非金属
带隙
1.081
eV
直接带隙
是
导带底 CBM
N/A
eV

磁性属性

磁性
否
总磁矩
0.0004
μB
磁有序
NM
磁性位点数
0
磁化强度/体积
0.000000
μB/ų

弹性属性

体积模量 KVRH
164.367
GPa
剪切模量 GVRH
62.670
GPa
杨氏模量 E
166.810
GPa
泊松比 ν
0.331
Pugh 比 K/G
2.623
维氏硬度估算
4.284
GPa
韧性
Brittle
各向异性
0.490
拉梅 λ
122.587
GPa

特征分析

组成概览

元素组成(原子分数)
组成信息
主元 / 基质元素
O · 0.600
元素家族
Metalloid-Nonmetal
溶质分数
0.400
间隙分数
0.000

弹性数据特征

弹性模量对比 (GPa)
弹性特征
弹性各向异性 AU
0.4900
均匀泊松比 ν
0.3310
Debye 温度
490.3
K
声速
3,467.1
m/s
均匀度
0.3310

关联任务

引用参考

Materials Project 官方页面
https://materialsproject.org/materials/mp-13334
参考引用

The crystal structure and related material properties of ZnSnO3 were obtained from the Materials Project database (MP-ID: mp-13334, database version v2026.04.13).

文献引用

Horton, M.K., Huck, P., Yang, R.X. et al. Accelerated data-driven materials science with the Materials Project. Nat. Mater. 24, 1522–1532 (2025). https://doi.org/10.1038/s41563-025-02272-0

BibTeX
@article{Horton2025,
  author  = {Horton, Matthew K. and Huck, Patrick and Yang, Ruo Xi and Munro, Jason M. and Dwaraknath, Shyam and Ganose, Alex M. and Kingsbury, Ryan S. and Wen, Mingjian and Shen, Jimmy X. and Mathis, Tyler S. and Kaplan, Aaron D. and Berket, Karlo and Riebesell, Janosh and George, Janine and Rosen, Andrew S. and Spotte-Smith, Evan W. C. and McDermott, Matthew J. and Cohen, Orion A. and Dunn, Alex and Kuner, Matthew C. and Rignanese, Gian-Marco and Petretto, Guido and Waroquiers, David and Griffin, Sinead M. and Neaton, Jeffrey B. and Chrzan, Daryl C. and Asta, Mark and Hautier, Geoffroy and Cholia, Shreyas and Ceder, Gerbrand and Ong, Shyue Ping and Jain, Anubhav and Persson, Kristin A.},
  title   = {Accelerated data-driven materials science with the Materials Project},
  journal = {Nature Materials},
  year    = {2025},
  volume  = {24},
  number  = {10},
  pages   = {1522--1532},
  month   = {oct},
  doi     = {10.1038/s41563-025-02272-0},
  issn    = {1476-4660},
  url     = {https://doi.org/10.1038/s41563-025-02272-0},
  abstract = {The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.}
}

晶体结构 3D 渲染

加载 3D 结构...

样式: Ball & Stick
原始 CIF 数据
# generated using pymatgen
data_ZnSnO3
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   5.65912198
_cell_length_b   5.65912198
_cell_length_c   5.65912176
_cell_angle_alpha   56.42809504
_cell_angle_beta   56.42809504
_cell_angle_gamma   56.42809210
_symmetry_Int_Tables_number   1
_chemical_formula_structural   ZnSnO3
_chemical_formula_sum   'Zn2 Sn2 O6'
_cell_volume   117.57007878
_cell_formula_units_Z   2
loop_
 _symmetry_equiv_pos_site_id
 _symmetry_equiv_pos_as_xyz
  1  'x, y, z'
loop_
 _atom_site_type_symbol
 _atom_site_label
 _atom_site_symmetry_multiplicity
 _atom_site_fract_x
 _atom_site_fract_y
 _atom_site_fract_z
 _atom_site_occupancy
  Zn  Zn0  1  0.71592800  0.71592800  0.71592800  1.0
  Zn  Zn1  1  0.21592800  0.21592800  0.21592800  1.0
  Sn  Sn2  1  0.99815600  0.99815600  0.99815600  1.0
  Sn  Sn3  1  0.49815600  0.49815600  0.49815600  1.0
  O  O4  1  0.78719200  0.10723100  0.39289400  1.0
  O  O5  1  0.10723100  0.39289400  0.78719200  1.0
  O  O6  1  0.39289400  0.78719200  0.10723100  1.0
  O  O7  1  0.28719200  0.89289400  0.60723100  1.0
  O  O8  1  0.60723100  0.28719200  0.89289400  1.0
  O  O9  1  0.89289400  0.60723100  0.28719200  1.0
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