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HEAS Data Platform · 材料科学计算数据库

LiC

弹性数据 ELA

MP-ID: mp-1378 · 空间群: Immm (#71)

基础信息

化学式
LiC
MP-ID
mp-1378
元素数
2
位点数
4
密度
1.3692
g/cm³
体积
45.97
ų
晶胞参数 a
3.5915
Å
晶胞参数 b
4.0117
Å
晶胞参数 c
4.0117
Å
α 角
73.52
°
β 角
63.41
°
γ 角
63.41
°
空间群
Immm
点群
mmm
晶体系统
Orthorhombic
布拉维格子
Orthorhombic I
稳定性
是
堆积分数
0.6800

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
24.96 3.5679 100.0 (0,1,0)
30.00 2.9786 68.4 (1,1,1)
30.00 2.9786 68.4 (1,0,0)
31.10 2.8761 5.7 (1,1,0)
33.62 2.6654 12.2 (0,1,1)
37.46 2.4010 5.4 (0,1,-1)
46.45 1.9550 38.5 (1,2,1)
46.45 1.9550 38.5 (1,0,-1)
50.85 1.7958 22.1 (2,1,1)
51.21 1.7839 7.2 (0,2,0)
55.11 1.6665 8.3 (0,2,1)
57.45 1.6041 6.6 (2,2,1)
57.45 1.6041 2.2 (2,0,1)
57.90 1.5927 4.0 (1,2,2)
57.90 1.5927 4.0 (1,-1,-1)
60.38 1.5330 5.9 (0,2,-1)
63.65 1.4620 14.6 (1,2,-1)
64.84 1.4380 2.3 (2,2,0)
70.69 1.3327 6.6 (0,2,2)
75.05 1.2656 1.0 (2,1,3)
75.05 1.2656 2.1 (2,3,1)
75.05 1.2656 1.0 (2,-1,1)
78.26 1.2215 1.2 (2,2,3)
78.26 1.2215 1.2 (2,3,2)
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能量属性

形成能/原子
-0.09988
eV/atom
能量/原子
-6.26685
eV/atom
凸包距离
0.0000
eV/atom
未修正能量
-6.2669
eV/atom
体积/原子
11.49
ų
c/a 比
-

电子属性

金属性
非金属
带隙
3.273
eV
直接带隙
否
导带底 CBM
N/A
eV

磁性属性

磁性
是
总磁矩
0.0000
μB
磁有序
Unknown
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
40.769
GPa
剪切模量 GVRH
30.156
GPa
杨氏模量 E
72.574
GPa
泊松比 ν
0.203
Pugh 比 K/G
1.352
维氏硬度估算
7.310
GPa
韧性
Ductile
各向异性
1.378
拉梅 λ
20.665
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
Li · 0.500
元素家族
Alkali-AlkEarth
溶质分数
0.500
间隙分数
0.500

弹性数据特征

弹性模量对比 (GPa)
弹性特征
弹性各向异性 AU
1.3780
均匀泊松比 ν
0.2030
Debye 温度
683.6
K
声速
5,182.9
m/s
均匀度
0.2030

关联任务

关联的计算任务 ID(3 个)

引用参考

Materials Project 官方页面
https://materialsproject.org/materials/mp-1378
参考引用

The crystal structure and related material properties of LiC were obtained from the Materials Project database (MP-ID: mp-1378, 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_LiC
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   3.59152978
_cell_length_b   4.01170411
_cell_length_c   4.01170449
_cell_angle_alpha   73.52458272
_cell_angle_beta   63.40817150
_cell_angle_gamma   63.40816767
_symmetry_Int_Tables_number   1
_chemical_formula_structural   LiC
_chemical_formula_sum   'Li2 C2'
_cell_volume   45.96833740
_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
  Li  Li0  1  0.76069444  0.73930556  0.73930556  1.0
  Li  Li1  1  0.23930556  0.26069444  0.26069444  1.0
  C  C2  1  0.00000000  0.86974406  0.13025594  1.0
  C  C3  1  -0.00000000  0.13025594  0.86974406  1.0
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