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HEAS Data Platform · 材料科学计算数据库

Tl

传统材料 TRA 弹性数据 ELA

MP-ID: mp-82 · 空间群: P6_3/mmc (#194)

基础信息

化学式
Tl
MP-ID
mp-82
元素数
1
位点数
2
密度
11.3484
g/cm³
体积
59.81
ų
晶胞参数 a
3.4973
Å
晶胞参数 b
3.4973
Å
晶胞参数 c
5.6466
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 24)
2θd (Å)Ihkl
29.49 3.0288 11.7 (1,0,-1,0)
29.49 3.0288 23.3 (1,-1,0,0)
31.69 2.8233 38.8 (0,0,0,2)
33.58 2.6691 50.0 (1,0,-1,1)
33.58 2.6691 100.0 (1,-1,0,1)
43.84 2.0652 8.1 (1,0,-1,2)
43.84 2.0652 16.1 (1,-1,0,2)
52.32 1.7487 9.7 (2,-1,-1,0)
52.32 1.7487 9.7 (1,1,-2,0)
52.32 1.7487 9.7 (1,-2,1,0)
57.66 1.5987 10.9 (1,0,-1,3)
57.66 1.5987 21.9 (1,-1,0,3)
61.20 1.5144 1.5 (2,0,-2,0)
61.20 1.5144 1.5 (2,-2,0,0)
61.20 1.5144 1.5 (0,2,-2,0)
62.47 1.4866 11.5 (2,-1,-1,2)
62.47 1.4866 11.5 (1,1,-2,2)
62.47 1.4866 11.5 (1,-2,1,2)
63.61 1.4627 8.2 (2,0,-2,1)
63.61 1.4627 8.2 (2,-2,0,1)
63.61 1.4627 8.2 (0,2,-2,1)
66.20 1.4116 4.8 (0,0,0,4)
70.57 1.3345 2.0 (2,0,-2,2)
70.57 1.3345 2.0 (2,-2,0,2)
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能量属性

形成能/原子
0.00000
eV/atom
能量/原子
-53.17930
eV/atom
凸包距离
0.0000
eV/atom
未修正能量
-53.1793
eV/atom
体积/原子
29.91
ų
c/a 比
1.6145

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
26.378
GPa
剪切模量 GVRH
11.153
GPa
杨氏模量 E
29.326
GPa
泊松比 ν
0.315
Pugh 比 K/G
2.365
维氏硬度估算
0.000
GPa
韧性
Brittle
各向异性
0.464
拉梅 λ
18.943
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
Tl · 1.000
元素家族
N/A
溶质分数
0.000
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
0.573
家族
Pure Metal
基质元素
Tl · 1.000

弹性数据特征

弹性模量对比 (GPa)
弹性特征
弹性各向异性 AU
0.4640
均匀泊松比 ν
0.3150
Debye 温度
106.4
K
声速
1,109.4
m/s
均匀度
0.3150

关联任务

引用参考

Materials Project 官方页面
https://materialsproject.org/materials/mp-82
参考引用

The crystal structure and related material properties of Tl were obtained from the Materials Project database (MP-ID: mp-82, 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_Tl
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   3.49734068
_cell_length_b   3.49733836
_cell_length_c   5.64656168
_cell_angle_alpha   90.00000000
_cell_angle_beta   90.00000000
_cell_angle_gamma   119.99999214
_symmetry_Int_Tables_number   1
_chemical_formula_structural   Tl
_chemical_formula_sum   Tl2
_cell_volume   59.81227666
_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
  Tl  Tl0  1  0.33333300  0.66666700  0.25000000  1.0
  Tl  Tl1  1  0.66666700  0.33333300  0.75000000  1.0
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