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Cu6OF11

高熵材料 HEA 传统材料 TRA

MP-ID: mp-752933 · 空间群: P1 (#1)

基础信息

化学式
Cu6OF11
MP-ID
mp-752933
元素数
3
位点数
18
密度
4.7244
g/cm³
体积
213.09
ų
晶胞参数 a
4.6443
Å
晶胞参数 b
5.4348
Å
晶胞参数 c
8.4564
Å
α 角
87.18
°
β 角
88.32
°
γ 角
89.39
°
空间群
P1
点群
1
晶体系统
Triclinic
布拉维格子
Triclinic 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
26.75 3.3325 100.0 (1,1,1)
27.41 3.2542 95.1 (1,-1,-1)
31.80 2.8142 50.4 (0,0,3)
33.35 2.6868 37.5 (1,-1,2)
33.89 2.6449 31.3 (1,1,-2)
35.23 2.5477 23.8 (0,2,-1)
36.87 2.4378 11.4 (1,0,3)
37.86 2.3764 9.3 (1,0,-3)
38.55 2.3355 9.7 (0,2,2)
38.80 2.3211 9.8 (2,0,0)
40.38 2.2335 6.5 (1,-2,1)
40.39 2.2334 6.4 (1,2,-1)
42.93 2.1066 2.1 (1,2,2)
43.10 2.0986 2.1 (2,1,1)
43.80 2.0667 2.5 (1,-2,-2)
43.97 2.0592 2.6 (2,-1,-1)
49.03 1.8580 20.4 (1,1,4)
50.21 1.8171 19.3 (1,-1,-4)
50.23 1.8165 16.7 (2,0,3)
51.77 1.7659 13.9 (2,0,-3)
53.40 1.7159 17.5 (2,-2,1)
53.40 1.7157 17.4 (2,2,-1)
54.24 1.6911 15.0 (1,3,0)
54.61 1.6806 13.5 (1,-3,0)
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能量属性

形成能/原子
-1.70131
eV/atom
能量/原子
-4.51262
eV/atom
凸包距离
0.0178
eV/atom
未修正能量
-4.1921
eV/atom
体积/原子
11.84
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
5.0011
μB
磁有序
FiM
磁性位点数
7
磁化强度/体积
0.023470
μB/ų

弹性属性

弹性数据暂不开放

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 · 0.611
元素家族
Metalloid-Nonmetal
溶质分数
0.389
间隙分数
0.000

高熵合金特征

综合指标雷达(归一化)
元素类别占比
混合熵 ΔS/R
0.8277
高熵判据(>1.0 视为高熵)
混合熵 ΔS
6.8818
J/mol·K
Ω 参数
0.0000
Ω > 1.1 利于固溶体形成
原子尺寸错配 δr
47.608%
δr < 6.6% 利于单相固溶
电负性差 Δχ
0.9670
越小混合越稳定
价电子浓度 VEC
6.944
价电子浓度,影响相稳定性
混合焓 ΔH_mix
0.000
kJ/mol 且负值为放热混合
熵归一化
0.7534
固溶体倾向
0
相趋势
IM

传统材料特征

ROM 混合特征雷达
评级与分类
评级 Tier
B
评分
0.596
家族
F-rich Multi-Component
基质元素
F · 0.611

关联任务

关联的计算任务 ID(2 个)

引用参考

Materials Project 官方页面
https://materialsproject.org/materials/mp-752933
参考引用

The crystal structure and related material properties of Cu6OF11 were obtained from the Materials Project database (MP-ID: mp-752933, 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_Cu6OF11
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   4.64432700
_cell_length_b   5.43483804
_cell_length_c   8.45641735
_cell_angle_alpha   87.17741839
_cell_angle_beta   88.32340664
_cell_angle_gamma   89.39394258
_symmetry_Int_Tables_number   1
_chemical_formula_structural   Cu6OF11
_chemical_formula_sum   'Cu6 O1 F11'
_cell_volume   213.09067821
_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
  Cu  Cu0  1  0.00287600  0.99910100  0.00120400  1.0
  Cu  Cu1  1  0.52295700  0.49908100  0.98143400  1.0
  Cu  Cu2  1  0.98766800  0.34253800  0.67416500  1.0
  Cu  Cu3  1  0.51162300  0.81796900  0.66943800  1.0
  Cu  Cu4  1  0.49544500  0.16879100  0.33517000  1.0
  Cu  Cu5  1  0.99882400  0.66601600  0.33070500  1.0
  O  O6  1  0.69978400  0.54154700  0.76091600  1.0
  F  F7  1  0.20238300  0.29046600  0.91510700  1.0
  F  F8  1  0.79288200  0.04762600  0.75299300  1.0
  F  F9  1  0.29968800  0.79739700  0.90695900  1.0
  F  F10  1  0.29129500  0.12362700  0.58424900  1.0
  F  F11  1  0.19496200  0.62168100  0.58066900  1.0
  F  F12  1  0.79537400  0.37489700  0.41752500  1.0
  F  F13  1  0.69667600  0.87313400  0.42674900  1.0
  F  F14  1  0.30374000  0.46151000  0.23648400  1.0
  F  F15  1  0.70200300  0.20540000  0.08992000  1.0
  F  F16  1  0.20274000  0.96053900  0.24629200  1.0
  F  F17  1  0.79907800  0.70867900  0.09002100  1.0
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