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LaBiO3

弹性数据 ELA

MP-ID: mp-768310 · 空间群: R3c (#161)

基础信息

化学式
LaBiO3
MP-ID
mp-768310
元素数
3
位点数
10
密度
7.5370
g/cm³
体积
174.44
ų
晶胞参数 a
6.2718
Å
晶胞参数 b
6.2718
Å
晶胞参数 c
6.2718
Å
α 角
60.00
°
β 角
60.00
°
γ 角
60.00
°
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
20.02 4.4348 23.4 (1,1,0)
28.46 3.1359 92.6 (2,1,1)
28.46 3.1358 100.0 (1,0,-1)
33.51 2.6743 1.9 (2,1,0)
35.05 2.5605 3.8 (2,2,2)
35.05 2.5604 2.7 (2,0,0)
35.05 2.5604 1.3 (0,0,2)
40.69 2.2174 42.0 (2,2,0)
45.75 1.9833 16.2 (3,2,1)
45.75 1.9833 8.1 (2,1,3)
45.75 1.9833 4.2 (2,1,-1)
50.40 1.8106 14.3 (3,3,2)
50.40 1.8105 29.7 (3,1,0)
50.40 1.8105 14.8 (1,0,3)
50.40 1.8105 28.0 (2,-1,-1)
58.90 1.5680 5.2 (4,2,2)
58.90 1.5680 2.6 (2,2,4)
58.90 1.5679 14.0 (2,0,-2)
62.86 1.4783 5.9 (4,3,3)
62.86 1.4783 2.9 (3,3,4)
62.87 1.4783 5.1 (4,1,1)
62.87 1.4783 1.0 (1,1,4)
62.87 1.4782 1.6 (3,0,-1)
66.69 1.4024 7.5 (4,3,1)
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能量属性

形成能/原子
-2.73383
eV/atom
能量/原子
-7.46782
eV/atom
凸包距离
0.0352
eV/atom
未修正能量
-7.0556
eV/atom
体积/原子
17.44
ų
c/a 比
1.0000

电子属性

金属性
非金属
带隙
3.070
eV
直接带隙
否
导带底 CBM
N/A
eV

磁性属性

磁性
否
总磁矩
0.0000
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
57.541
GPa
剪切模量 GVRH
15.033
GPa
杨氏模量 E
41.486
GPa
泊松比 ν
0.380
Pugh 比 K/G
3.828
维氏硬度估算
0.000
GPa
韧性
Brittle
各向异性
4.044
拉梅 λ
47.519
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
4.0440
均匀泊松比 ν
0.3800
Debye 温度
191.0
K
声速
1,594.3
m/s
均匀度
0.3800

关联任务

引用参考

Materials Project 官方页面
https://materialsproject.org/materials/mp-768310
参考引用

The crystal structure and related material properties of LaBiO3 were obtained from the Materials Project database (MP-ID: mp-768310, 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_LaBiO3
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   6.27184036
_cell_length_b   6.27184036
_cell_length_c   6.27183942
_cell_angle_alpha   59.99759032
_cell_angle_beta   59.99759032
_cell_angle_gamma   59.99759113
_symmetry_Int_Tables_number   1
_chemical_formula_structural   LaBiO3
_chemical_formula_sum   'La2 Bi2 O6'
_cell_volume   174.44004834
_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
  La  La0  1  0.71972600  0.71972600  0.71972600  1.0
  La  La1  1  0.21972600  0.21972600  0.21972600  1.0
  Bi  Bi2  1  0.99947800  0.99947800  0.99947800  1.0
  Bi  Bi3  1  0.49947800  0.49947800  0.49947800  1.0
  O  O4  1  0.82738900  0.09622900  0.37545700  1.0
  O  O5  1  0.59622900  0.32738900  0.87545700  1.0
  O  O6  1  0.87545700  0.59622900  0.32738900  1.0
  O  O7  1  0.09622900  0.37545700  0.82738900  1.0
  O  O8  1  0.37545700  0.82738900  0.09622900  1.0
  O  O9  1  0.32738900  0.87545700  0.59622900  1.0
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