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HEAS Data Platform · 材料科学计算数据库

Mn3OF5

高熵材料 HEA 传统材料 TRA

MP-ID: mp-780664 · 空间群: C2/m (#12)

基础信息

化学式
Mn3OF5
MP-ID
mp-780664
元素数
3
位点数
18
密度
3.9126
g/cm³
体积
234.11
ų
晶胞参数 a
5.8708
Å
晶胞参数 b
5.8708
Å
晶胞参数 c
7.5796
Å
α 角
73.36
°
β 角
73.36
°
γ 角
73.66
°
空间群
C2/m
点群
2/m
晶体系统
Monoclinic
布拉维格子
Monoclinic C
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
25.31 3.5194 100.0 (1,-1,0)
26.09 3.4158 95.9 (1,1,2)
33.04 2.7112 48.5 (1,0,-2)
33.16 2.7014 51.7 (2,1,0)
36.21 2.4809 1.5 (1,1,3)
36.66 2.4511 13.0 (2,0,2)
37.70 2.3860 21.6 (2,0,-1)
37.99 2.3685 2.5 (1,0,3)
38.14 2.3594 9.9 (0,0,3)
38.36 2.3465 10.9 (2,2,1)
40.66 2.2190 7.7 (2,-1,1)
41.69 2.1666 5.8 (2,1,3)
44.69 2.0277 2.0 (2,0,3)
45.09 2.0107 1.1 (1,0,-3)
45.27 2.0033 1.7 (2,2,3)
49.73 1.8334 33.1 (2,-1,-2)
49.82 1.8303 34.5 (3,0,0)
50.53 1.8064 29.6 (1,0,4)
50.79 1.7977 27.6 (3,2,2)
51.97 1.7597 21.1 (2,-2,0)
52.57 1.7410 2.2 (3,1,3)
53.67 1.7079 15.8 (2,2,4)
56.72 1.6229 20.6 (2,2,-2)
59.05 1.5643 10.0 (3,-1,2)
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能量属性

形成能/原子
-2.72200
eV/atom
能量/原子
-7.38943
eV/atom
凸包距离
0.0538
eV/atom
未修正能量
-6.5004
eV/atom
体积/原子
13.01
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
28.0023
μB
磁有序
FM
磁性位点数
6
磁化强度/体积
0.119610
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 · 0.556
元素家族
Metalloid-Nonmetal
溶质分数
0.444
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
0.9369
高熵判据(>1.0 视为高熵)
混合熵 ΔS
7.7893
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
46.423%
δr < 6.6% 利于单相固溶
电负性差 Δχ
1.1153
越小混合越稳定
价电子浓度 VEC
5.556
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
0.8528
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
0.507
家族
F-rich Multi-Component
基质元素
F · 0.556

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-780664
参考引用

The crystal structure and related material properties of Mn3OF5 were obtained from the Materials Project database (MP-ID: mp-780664, 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_Mn3OF5
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   5.87084470
_cell_length_b   5.87084470
_cell_length_c   7.57960039
_cell_angle_alpha   73.36360693
_cell_angle_beta   73.36360693
_cell_angle_gamma   73.66350039
_symmetry_Int_Tables_number   1
_chemical_formula_structural   Mn3OF5
_chemical_formula_sum   'Mn6 O2 F10'
_cell_volume   234.11098967
_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
  Mn  Mn0  1  0.67879800  0.67879800  0.84817400  1.0
  Mn  Mn1  1  0.33281700  0.33281700  0.68259600  1.0
  Mn  Mn2  1  0.66718300  0.66718300  0.31740400  1.0
  Mn  Mn3  1  0.32120200  0.32120200  0.15182600  1.0
  Mn  Mn4  1  0.00000000  0.00000000  0.50000000  1.0
  Mn  Mn5  1  0.00000000  0.00000000  0.00000000  1.0
  O  O6  1  0.21400000  0.21400000  0.96421800  1.0
  O  O7  1  0.78600000  0.78600000  0.03578200  1.0
  F  F8  1  0.63370600  0.03331700  0.66376600  1.0
  F  F9  1  0.96668300  0.36629400  0.33623400  1.0
  F  F10  1  0.90722900  0.90722900  0.29084000  1.0
  F  F11  1  0.57510100  0.57510100  0.62543700  1.0
  F  F12  1  0.09277100  0.09277100  0.70916000  1.0
  F  F13  1  0.42489900  0.42489900  0.37456300  1.0
  F  F14  1  0.36629400  0.96668300  0.33623400  1.0
  F  F15  1  0.03331700  0.63370600  0.66376600  1.0
  F  F16  1  0.69356400  0.30643600  0.00000000  1.0
  F  F17  1  0.30643600  0.69356400  0.00000000  1.0
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