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CuSiO3

弹性数据 ELA

MP-ID: mp-16053 · 空间群: Pmma (#51)

基础信息

化学式
CuSiO3
MP-ID
mp-16053
元素数
3
位点数
10
密度
3.6798
g/cm³
体积
126.02
ų
晶胞参数 a
2.8603
Å
晶胞参数 b
4.9182
Å
晶胞参数 c
8.9581
Å
α 角
90.00
°
β 角
90.00
°
γ 角
90.00
°
空间群
Pmma
点群
mmm
晶体系统
Orthorhombic
布拉维格子
Orthorhombic P
稳定性
否
堆积分数
0.5240

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
18.04 4.9182 18.7 (0,1,0)
19.82 4.4791 9.2 (0,0,2)
20.60 4.3112 18.8 (0,1,1)
26.92 3.3116 100.0 (0,1,2)
31.27 2.8603 1.5 (1,0,0)
35.16 2.5524 1.0 (0,1,3)
36.33 2.4725 14.6 (1,1,0)
36.54 2.4591 5.9 (0,2,0)
37.30 2.4107 16.6 (1,0,2)
37.74 2.3834 12.0 (1,1,1)
40.27 2.2395 10.7 (0,0,4)
41.73 2.1646 17.7 (1,1,2)
41.91 2.1556 5.5 (0,2,2)
44.45 2.0382 1.4 (0,1,4)
47.92 1.8983 2.2 (0,2,3)
48.84 1.8647 16.0 (1,2,0)
49.96 1.8256 2.0 (1,2,1)
51.85 1.7633 7.5 (1,0,4)
53.21 1.7215 10.2 (1,2,2)
55.35 1.6599 14.1 (1,1,4)
55.50 1.6558 9.6 (0,2,4)
56.10 1.6394 1.6 (0,3,0)
58.34 1.5816 5.1 (1,2,3)
60.10 1.5395 1.8 (0,3,2)
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能量属性

形成能/原子
-2.24962
eV/atom
能量/原子
-7.12330
eV/atom
凸包距离
0.0912
eV/atom
未修正能量
-6.7111
eV/atom
体积/原子
12.60
ų
c/a 比
-

电子属性

金属性
非金属
带隙
0.112
eV
直接带隙
否
导带底 CBM
N/A
eV

磁性属性

磁性
是
总磁矩
2.0112
μB
磁有序
FM
磁性位点数
2
磁化强度/体积
0.015960
μB/ų

弹性属性

体积模量 KVRH
31.454
GPa
剪切模量 GVRH
19.986
GPa
杨氏模量 E
49.478
GPa
泊松比 ν
0.238
Pugh 比 K/G
1.574
维氏硬度估算
3.785
GPa
韧性
Ductile
各向异性
23.030
拉梅 λ
18.130
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
0.200

弹性数据特征

弹性模量对比 (GPa)
弹性特征
弹性各向异性 AU
23.0300
均匀泊松比 ν
0.2380
Debye 温度
380.3
K
声速
2,583.7
m/s
均匀度
0.2380

关联任务

引用参考

Materials Project 官方页面
https://materialsproject.org/materials/mp-16053
参考引用

The crystal structure and related material properties of CuSiO3 were obtained from the Materials Project database (MP-ID: mp-16053, 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_CuSiO3
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   2.86025300
_cell_length_b   4.91823600
_cell_length_c   8.95813900
_cell_angle_alpha   90.00000000
_cell_angle_beta   90.00000000
_cell_angle_gamma   90.00000000
_symmetry_Int_Tables_number   1
_chemical_formula_structural   CuSiO3
_chemical_formula_sum   'Cu2 Si2 O6'
_cell_volume   126.01771806
_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
  Cu  Cu0  1  0.00000000  0.50000000  0.50000000  1.0
  Cu  Cu1  1  0.00000000  0.50000000  0.00000000  1.0
  Si  Si2  1  0.50000000  0.87785900  0.75000000  1.0
  Si  Si3  1  0.50000000  0.12214100  0.25000000  1.0
  O  O4  1  0.50000000  0.69565700  0.59725400  1.0
  O  O5  1  0.50000000  0.30434300  0.40274600  1.0
  O  O6  1  0.50000000  0.69565700  0.90274600  1.0
  O  O7  1  0.50000000  0.30434300  0.09725400  1.0
  O  O8  1  0.00000000  0.94523200  0.25000000  1.0
  O  O9  1  0.00000000  0.05476800  0.75000000  1.0
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