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TlPF6

弹性数据 ELA

MP-ID: mp-675283 · 空间群: R-3m (#166)

基础信息

化学式
TlPF6
MP-ID
mp-675283
元素数
3
位点数
8
密度
4.9892
g/cm³
体积
116.27
ų
晶胞参数 a
5.3120
Å
晶胞参数 b
5.6609
Å
晶胞参数 c
5.3120
Å
α 角
62.02
°
β 角
60.00
°
γ 角
62.02
°
空间群
R-3m
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
18.65 4.7581 13.8 (0,1,0)
20.28 4.3785 10.2 (0,0,1)
20.28 4.3785 20.4 (1,1,1)
23.00 3.8667 100.0 (1,1,0)
31.73 2.8197 32.2 (1,-1,0)
31.73 2.8197 16.1 (1,2,1)
33.75 2.6560 16.8 (1,1,2)
33.75 2.6560 8.4 (2,1,1)
37.06 2.4257 21.9 (1,-1,1)
37.82 2.3791 3.1 (0,2,0)
38.83 2.3191 19.2 (1,0,2)
38.83 2.3191 9.6 (2,0,1)
39.69 2.2709 9.0 (0,1,2)
39.69 2.2709 18.1 (2,1,2)
41.24 2.1893 8.6 (0,0,2)
41.24 2.1893 17.1 (2,2,2)
47.00 1.9333 5.5 (2,2,0)
48.85 1.8643 9.3 (1,-2,0)
51.57 1.7721 20.9 (2,-1,1)
51.57 1.7721 4.2 (2,3,1)
53.06 1.7260 6.8 (1,1,3)
53.06 1.7260 3.4 (3,2,2)
54.30 1.6893 5.9 (2,1,3)
54.30 1.6893 5.9 (2,1,-1)
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能量属性

形成能/原子
-2.81044
eV/atom
能量/原子
-5.21974
eV/atom
凸包距离
0.0017
eV/atom
未修正能量
-12.7699
eV/atom
体积/原子
14.53
ų
c/a 比
1.0000

电子属性

金属性
非金属
带隙
6.028
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
13.435
GPa
剪切模量 GVRH
2.746
GPa
杨氏模量 E
7.713
GPa
泊松比 ν
0.404
Pugh 比 K/G
4.893
维氏硬度估算
0.000
GPa
韧性
Brittle
各向异性
130.426
拉梅 λ
11.604
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
F · 0.750
元素家族
Metalloid-Nonmetal
溶质分数
0.250
间隙分数
0.125

弹性数据特征

弹性模量对比 (GPa)
弹性特征
弹性各向异性 AU
130.4260
均匀泊松比 ν
0.4040
Debye 温度
116.9
K
声速
840.3
m/s
均匀度
0.4040

关联任务

引用参考

Materials Project 官方页面
https://materialsproject.org/materials/mp-675283
参考引用

The crystal structure and related material properties of TlPF6 were obtained from the Materials Project database (MP-ID: mp-675283, 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_TlPF6
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   5.31195570
_cell_length_b   5.66087366
_cell_length_c   5.31195691
_cell_angle_alpha   62.01881793
_cell_angle_beta   60.00000488
_cell_angle_gamma   62.01881646
_symmetry_Int_Tables_number   1
_chemical_formula_structural   TlPF6
_chemical_formula_sum   'Tl1 P1 F6'
_cell_volume   116.27238650
_cell_formula_units_Z   1
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
  Tl  Tl0  1  0.50000000  0.50000000  0.50000000  1.0
  P  P1  1  -0.00000000  0.00000000  0.00000000  1.0
  F  F2  1  0.07849185  0.19627063  0.07849185  1.0
  F  F3  1  0.07849185  0.19627063  0.64674567  1.0
  F  F4  1  0.64674567  0.19627063  0.07849185  1.0
  F  F5  1  0.35325433  0.80372937  0.92150815  1.0
  F  F6  1  0.92150815  0.80372937  0.35325433  1.0
  F  F7  1  0.92150815  0.80372937  0.92150815  1.0
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