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Fe(NF2)3

高熵材料 HEA 传统材料 TRA

MP-ID: mp-723385 · 空间群: Fm-3m (#225)

基础信息

化学式
Fe(NF2)3
MP-ID
mp-723385
元素数
3
位点数
10
密度
3.0391
g/cm³
体积
115.76
ų
晶胞参数 a
5.4704
Å
晶胞参数 b
5.4704
Å
晶胞参数 c
5.4704
Å
α 角
60.00
°
β 角
60.00
°
γ 角
60.00
°
空间群
Fm-3m
点群
m-3m
晶体系统
Cubic
布拉维格子
Cubic F
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 17)
2θd (Å)Ihkl
19.88 4.4666 37.1 (1,1,1)
22.99 3.8682 100.0 (1,1,0)
32.74 2.7352 40.2 (2,1,1)
38.60 2.3326 30.9 (2,2,1)
40.39 2.2333 18.8 (2,2,2)
46.98 1.9341 79.0 (2,2,0)
51.49 1.7748 17.4 (3,2,2)
52.93 1.7299 34.7 (3,2,1)
58.44 1.5792 11.8 (3,3,2)
62.37 1.4889 7.8 (3,3,1)
68.62 1.3676 37.9 (4,2,2)
72.25 1.3077 7.0 (4,3,2)
73.44 1.2894 14.0 (4,1,1)
78.14 1.2232 5.0 (3,2,-1)
81.60 1.1798 3.2 (4,4,3)
82.75 1.1663 2.2 (4,4,2)
87.32 1.1166 11.3 (4,4,4)
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能量属性

形成能/原子
0.25628
eV/atom
能量/原子
-4.24095
eV/atom
凸包距离
1.4649
eV/atom
未修正能量
-3.7382
eV/atom
体积/原子
11.58
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
10.7236
μB
磁有序
FM
磁性位点数
3
磁化强度/体积
0.092640
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 · 0.600
元素家族
Metalloid-Nonmetal
溶质分数
0.400
间隙分数
0.300

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
0.8979
高熵判据(>1.0 视为高熵)
混合熵 ΔS
7.4655
J/mol·K
Ω 参数
0.4814
Ω > 1.1 利于固溶体形成
原子尺寸错配 δr
35.599%
δr < 6.6% 利于单相固溶
电负性差 Δχ
0.6931
越小混合越稳定
价电子浓度 VEC
5.300
价电子浓度,影响相稳定性
混合焓 ΔH_mix
-3.600
kJ/mol 且负值为放热混合
熵归一化
0.8173
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
C
评分
0.372
家族
Interstitial Alloy
基质元素
F · 0.600

关联任务

引用参考

Materials Project 官方页面
https://materialsproject.org/materials/mp-723385
参考引用

The crystal structure and related material properties of Fe(NF2)3 were obtained from the Materials Project database (MP-ID: mp-723385, 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_Fe(NF2)3
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   5.47040858
_cell_length_b   5.47040858
_cell_length_c   5.47040858
_cell_angle_alpha   60.00000000
_cell_angle_beta   60.00000000
_cell_angle_gamma   60.00000000
_symmetry_Int_Tables_number   1
_chemical_formula_structural   Fe(NF2)3
_chemical_formula_sum   'Fe1 N3 F6'
_cell_volume   115.75620895
_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
  Fe  Fe0  1  0.00000000  0.00000000  0.00000000  1.0
  N  N1  1  0.50000000  0.50000000  0.50000000  1.0
  N  N2  1  0.25000000  0.25000000  0.25000000  1.0
  N  N3  1  0.75000000  0.75000000  0.75000000  1.0
  F  F4  1  0.74217100  0.25782900  0.25782900  1.0
  F  F5  1  0.74217100  0.25782900  0.74217100  1.0
  F  F6  1  0.74217100  0.74217100  0.25782900  1.0
  F  F7  1  0.25782900  0.74217100  0.74217100  1.0
  F  F8  1  0.25782900  0.74217100  0.25782900  1.0
  F  F9  1  0.25782900  0.25782900  0.74217100  1.0
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