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HEAS Data Platform · 材料科学计算数据库

TcIr3

传统材料 TRA 弹性数据 ELA

MP-ID: mp-867345 · 空间群: P6_3/mmc (#194)

基础信息

化学式
TcIr3
MP-ID
mp-867345
元素数
2
位点数
8
密度
19.7149
g/cm³
体积
113.65
ų
晶胞参数 a
5.4825
Å
晶胞参数 b
5.4825
Å
晶胞参数 c
4.3659
Å
α 角
90.00
°
β 角
90.00
°
γ 角
120.00
°
空间群
P6_3/mmc
点群
6/mmm
晶体系统
Hexagonal
布拉维格子
Hexagonal P
稳定性
是
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 18)
2θd (Å)Ihkl
18.69 4.7480 2.4 (1,0,-1,0)
27.76 3.2138 5.5 (1,0,-1,1)
32.67 2.7412 2.5 (2,-1,-1,0)
37.90 2.3740 24.3 (2,0,-2,0)
41.36 2.1830 25.4 (0,0,0,2)
43.39 2.0856 100.0 (2,0,-2,1)
53.67 1.7077 1.3 (2,-1,-1,2)
55.35 1.6598 1.9 (3,-1,-2,1)
57.34 1.6069 14.7 (2,0,-2,2)
68.45 1.3706 11.6 (4,-2,-2,0)
68.45 1.3706 5.8 (2,-4,2,0)
76.83 1.2407 18.8 (2,0,-2,3)
81.00 1.1870 1.8 (4,-4,0,0)
83.23 1.1608 6.9 (2,2,-4,2)
83.23 1.1608 13.8 (4,-2,-2,2)
84.61 1.1454 5.0 (4,0,-4,1)
84.61 1.1454 10.0 (4,-4,0,1)
89.87 1.0915 3.0 (0,0,0,4)
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能量属性

形成能/原子
-0.17894
eV/atom
能量/原子
-45.93962
eV/atom
凸包距离
0.0000
eV/atom
未修正能量
-45.9396
eV/atom
体积/原子
14.21
ų
c/a 比
0.7963

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
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
341.997
GPa
剪切模量 GVRH
157.989
GPa
杨氏模量 E
410.721
GPa
泊松比 ν
0.300
Pugh 比 K/G
2.165
维氏硬度估算
12.661
GPa
韧性
Brittle
各向异性
0.460
拉梅 λ
236.671
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
Ir · 0.750
元素家族
N/A
溶质分数
0.250
间隙分数
0.000

传统材料特征

ROM 混合特征雷达
评级与分类
评级 Tier
A
评分
0.641
家族
Binary Alloy
基质元素
Ir · 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
0.4600
均匀泊松比 ν
0.3000
Debye 温度
409.5
K
声速
3,161.9
m/s
均匀度
0.3000

关联任务

引用参考

Materials Project 官方页面
https://materialsproject.org/materials/mp-867345
参考引用

The crystal structure and related material properties of TcIr3 were obtained from the Materials Project database (MP-ID: mp-867345, 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_TcIr3
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   5.48248726
_cell_length_b   5.48248697
_cell_length_c   4.36594059
_cell_angle_alpha   90.00000000
_cell_angle_beta   90.00000000
_cell_angle_gamma   119.99999637
_symmetry_Int_Tables_number   1
_chemical_formula_structural   TcIr3
_chemical_formula_sum   'Tc2 Ir6'
_cell_volume   113.64850014
_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
  Tc  Tc0  1  0.33333408  0.66666489  0.75000000  1.0
  Tc  Tc1  1  0.66666775  0.33333376  0.25000000  1.0
  Ir  Ir2  1  0.16646858  0.33294019  0.25000000  1.0
  Ir  Ir3  1  0.66705853  0.83352809  0.25000000  1.0
  Ir  Ir4  1  0.16646887  0.83352802  0.25000000  1.0
  Ir  Ir5  1  0.83352875  0.66705971  0.75000000  1.0
  Ir  Ir6  1  0.33294085  0.16647227  0.75000000  1.0
  Ir  Ir7  1  0.83352959  0.16646806  0.75000000  1.0
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