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HEAS Data Platform · 材料科学计算数据库

RuN2

传统材料 TRA 弹性数据 ELA

MP-ID: mp-607371 · 空间群: Pnnm (#58)

基础信息

化学式
RuN2
MP-ID
mp-607371
元素数
2
位点数
6
密度
7.7786
g/cm³
体积
55.11
ų
晶胞参数 a
2.7171
Å
晶胞参数 b
4.1132
Å
晶胞参数 c
4.9313
Å
α 角
90.00
°
β 角
90.00
°
γ 角
90.00
°
空间群
Pnnm
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 20)
2θd (Å)Ihkl
28.25 3.1587 100.0 (0,1,1)
36.44 2.4657 44.8 (0,0,2)
37.80 2.3798 40.6 (1,0,1)
39.76 2.2671 79.9 (1,1,0)
42.76 2.1148 1.1 (0,1,2)
44.03 2.0566 21.8 (0,2,0)
55.02 1.6689 49.2 (1,1,2)
58.44 1.5793 18.2 (0,2,2)
59.40 1.5561 33.7 (1,2,1)
60.67 1.5264 16.8 (0,1,3)
66.48 1.4064 12.8 (1,0,3)
69.15 1.3586 7.6 (2,0,0)
71.41 1.3210 11.9 (0,3,1)
76.30 1.2480 12.7 (2,1,1)
77.41 1.2328 3.0 (0,0,4)
78.07 1.2241 5.9 (1,3,0)
80.77 1.1899 8.0 (2,0,2)
83.22 1.1609 12.9 (1,2,3)
85.70 1.1336 6.2 (2,2,0)
89.36 1.0964 10.1 (1,3,2)
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能量属性

形成能/原子
0.12491
eV/atom
能量/原子
-14.43577
eV/atom
凸包距离
0.1249
eV/atom
未修正能量
-8.2836
eV/atom
体积/原子
9.19
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
否
总磁矩
0.0005
μB
磁有序
NM
磁性位点数
0
磁化强度/体积
0.000010
μB/ų

弹性属性

体积模量 KVRH
279.032
GPa
剪切模量 GVRH
161.786
GPa
杨氏模量 E
406.746
GPa
泊松比 ν
0.257
Pugh 比 K/G
1.725
维氏硬度估算
17.716
GPa
韧性
Ductile
各向异性
1.219
拉梅 λ
171.175
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
N · 0.667
元素家族
N/A
溶质分数
0.333
间隙分数
0.667

传统材料特征

ROM 混合特征雷达
评级与分类
评级 Tier
A
评分
0.619
家族
Binary Alloy
基质元素
N · 0.667

弹性数据特征

弹性模量对比 (GPa)
弹性特征
弹性各向异性 AU
1.2190
均匀泊松比 ν
0.2570
Debye 温度
720.2
K
声速
5,067.3
m/s
均匀度
0.2570

关联任务

引用参考

Materials Project 官方页面
https://materialsproject.org/materials/mp-607371
参考引用

The crystal structure and related material properties of RuN2 were obtained from the Materials Project database (MP-ID: mp-607371, 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_RuN2
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   2.71709200
_cell_length_b   4.11322200
_cell_length_c   4.93130100
_cell_angle_alpha   90.00000000
_cell_angle_beta   90.00000000
_cell_angle_gamma   90.00000000
_symmetry_Int_Tables_number   1
_chemical_formula_structural   RuN2
_chemical_formula_sum   'Ru2 N4'
_cell_volume   55.11223275
_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
  Ru  Ru0  1  0.00000000  0.00000000  0.00000000  1.0
  Ru  Ru1  1  0.50000000  0.50000000  0.50000000  1.0
  N  N2  1  0.50000000  0.37939300  0.90884300  1.0
  N  N3  1  0.00000000  0.12060700  0.40884300  1.0
  N  N4  1  0.50000000  0.62060700  0.09115700  1.0
  N  N5  1  0.00000000  0.87939300  0.59115700  1.0
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