导航

HEAS Data Platform · 材料科学计算数据库

Fe3C

传统材料 TRA 弹性数据 ELA

MP-ID: mp-613523 · 空间群: Pnma (#62)

基础信息

化学式
Fe3C
MP-ID
mp-613523
元素数
2
位点数
16
密度
7.2053
g/cm³
体积
165.51
ų
晶胞参数 a
4.6572
Å
晶胞参数 b
5.0473
Å
晶胞参数 c
7.0413
Å
α 角
90.00
°
β 角
90.00
°
γ 角
90.00
°
空间群
Pnma
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
25.30 3.5207 3.8 (0,0,2)
26.03 3.4227 1.4 (1,1,0)
29.01 3.0783 9.4 (1,1,1)
36.62 2.4541 22.4 (1,1,2)
37.87 2.3757 46.4 (0,2,1)
38.67 2.3286 10.1 (2,0,0)
40.66 2.2188 16.0 (1,2,0)
42.73 2.1162 39.7 (1,2,1)
42.77 2.1144 61.8 (2,1,0)
43.16 2.0960 100.0 (1,0,3)
44.15 2.0511 23.2 (0,2,2)
44.75 2.0251 48.6 (2,1,1)
46.94 1.9357 33.8 (1,1,3)
48.50 1.8771 37.2 (1,2,2)
50.34 1.8126 8.3 (2,1,2)
53.30 1.7187 10.3 (0,2,3)
55.24 1.6630 4.1 (2,2,1)
58.31 1.5824 16.0 (1,3,0)
59.01 1.5654 8.8 (1,1,4)
59.91 1.5438 2.3 (1,3,1)
60.12 1.5392 5.4 (2,2,2)
64.14 1.4519 2.1 (3,1,1)
64.55 1.4438 7.9 (0,2,4)
64.57 1.4433 2.0 (1,3,2)
获取直接链接

能量属性

形成能/原子
0.12434
eV/atom
能量/原子
-8.53593
eV/atom
凸包距离
0.1243
eV/atom
未修正能量
-8.5359
eV/atom
体积/原子
10.34
ų
c/a 比
-

电子属性

金属性
金属
带隙
0
eV
直接带隙
否
导带底 CBM
N/A
eV

磁性属性

磁性
是
总磁矩
22.6677
μB
磁有序
FM
磁性位点数
12
磁化强度/体积
0.136950
μB/ų

弹性属性

体积模量 KVRH
180.669
GPa
剪切模量 GVRH
62.540
GPa
杨氏模量 E
168.211
GPa
泊松比 ν
0.345
Pugh 比 K/G
2.889
维氏硬度估算
3.497
GPa
韧性
Brittle
各向异性
0.868
拉梅 λ
138.976
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
Fe · 0.750
元素家族
N/A
溶质分数
0.250
间隙分数
0.250

传统材料特征

ROM 混合特征雷达
评级与分类
评级 Tier
A
评分
0.878
家族
Steel
基质元素
Fe · 0.750

弹性数据特征

弹性模量对比 (GPa)
弹性特征
弹性各向异性 AU
0.8680
均匀泊松比 ν
0.3450
Debye 温度
452.1
K
声速
3,310.1
m/s
均匀度
0.3450

关联任务

引用参考

Materials Project 官方页面
https://materialsproject.org/materials/mp-613523
参考引用

The crystal structure and related material properties of Fe3C were obtained from the Materials Project database (MP-ID: mp-613523, 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_Fe3C
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   4.65716400
_cell_length_b   5.04730000
_cell_length_c   7.04132900
_cell_angle_alpha   90.00000000
_cell_angle_beta   90.00000000
_cell_angle_gamma   90.00000000
_symmetry_Int_Tables_number   1
_chemical_formula_structural   Fe3C
_chemical_formula_sum   'Fe12 C4'
_cell_volume   165.51421077
_cell_formula_units_Z   4
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
  Fe  Fe0  1  0.34729400  0.16863600  0.42247800  1.0
  Fe  Fe1  1  0.15270600  0.66863600  0.07752200  1.0
  Fe  Fe2  1  0.84729400  0.33136400  0.92247800  1.0
  Fe  Fe3  1  0.33966400  0.45020900  0.75000000  1.0
  Fe  Fe4  1  0.15270600  0.66863600  0.42247800  1.0
  Fe  Fe5  1  0.34729400  0.16863600  0.07752200  1.0
  Fe  Fe6  1  0.16033600  0.95020900  0.75000000  1.0
  Fe  Fe7  1  0.65270600  0.83136400  0.92247800  1.0
  Fe  Fe8  1  0.84729400  0.33136400  0.57752200  1.0
  Fe  Fe9  1  0.66033600  0.54979100  0.25000000  1.0
  Fe  Fe10  1  0.65270600  0.83136400  0.57752200  1.0
  Fe  Fe11  1  0.83966400  0.04979100  0.25000000  1.0
  C  C12  1  0.00000000  0.00000000  0.50000000  1.0
  C  C13  1  0.50000000  0.50000000  0.50000000  1.0
  C  C14  1  0.50000000  0.50000000  0.00000000  1.0
  C  C15  1  0.00000000  0.00000000  0.00000000  1.0
获取直接链接