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Ca2Sn2F3

高熵材料 HEA 传统材料 TRA

MP-ID: mp-674232 · 空间群: Cm (#8)

基础信息

化学式
Ca2Sn2F3
MP-ID
mp-674232
元素数
3
位点数
14
密度
3.6376
g/cm³
体积
341.98
ų
晶胞参数 a
7.8689
Å
晶胞参数 b
7.8689
Å
晶胞参数 c
6.5359
Å
α 角
85.25
°
β 角
85.25
°
γ 角
58.09
°
空间群
Cm
点群
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
12.93 6.8484 49.4 (1,1,-2,0)
13.27 6.6724 15.0 (1,0,-1,0)
13.61 6.5065 62.1 (0,0,0,1)
17.89 4.9575 20.6 (1,1,-2,1)
18.60 4.7697 4.5 (1,0,-1,1)
19.69 4.5085 30.4 (1,1,-2,-1)
22.69 3.9192 4.0 (2,1,-3,0)
25.56 3.4848 7.7 (2,1,-3,1)
26.02 3.4242 100.0 (2,2,-4,0)
26.72 3.3362 11.4 (2,0,-2,0)
27.07 3.2943 43.4 (1,-1,0,1)
27.42 3.2533 14.6 (0,0,0,2)
27.51 3.2426 6.2 (2,1,-3,-1)
28.28 3.1560 8.5 (2,2,-4,1)
29.25 3.0528 23.8 (1,1,-2,2)
29.52 3.0259 62.3 (2,0,-2,1)
30.00 2.9789 16.0 (1,0,-1,2)
30.63 2.9183 42.6 (2,2,-4,-1)
30.68 2.9146 57.1 (2,0,-2,-1)
31.14 2.8724 10.9 (1,0,-1,-2)
31.54 2.8362 2.2 (1,1,-2,-2)
34.36 2.6097 15.0 (2,1,-3,2)
35.20 2.5498 3.1 (3,1,-4,0)
35.87 2.5038 10.3 (2,-1,-1,0)
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能量属性

形成能/原子
-2.51707
eV/atom
能量/原子
-5.05622
eV/atom
凸包距离
0.3135
eV/atom
未修正能量
-4.8582
eV/atom
体积/原子
24.43
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
否
总磁矩
0.0007
μB
磁有序
NM
磁性位点数
0
磁化强度/体积
0.000000
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 · 0.429
元素家族
Metalloid-Nonmetal
溶质分数
0.571
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
1.0790
高熵判据(>1.0 视为高熵)
混合熵 ΔS
8.9707
J/mol·K
Ω 参数
0.3633
Ω > 1.1 利于固溶体形成
原子尺寸错配 δr
52.773%
δr < 6.6% 利于单相固溶
电负性差 Δχ
1.2893
越小混合越稳定
价电子浓度 VEC
3.857
价电子浓度,影响相稳定性
混合焓 ΔH_mix
-11.997
kJ/mol 且负值为放热混合
熵归一化
0.9821
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
0.270
家族
F-rich Multi-Component
基质元素
F · 0.429

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-674232
参考引用

The crystal structure and related material properties of Ca2Sn2F3 were obtained from the Materials Project database (MP-ID: mp-674232, 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_Ca2Sn2F3
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   7.86886062
_cell_length_b   7.86886062
_cell_length_c   6.53593856
_cell_angle_alpha   85.24705868
_cell_angle_beta   85.24705868
_cell_angle_gamma   58.08667741
_symmetry_Int_Tables_number   1
_chemical_formula_structural   Ca2Sn2F3
_chemical_formula_sum   'Ca4 Sn4 F6'
_cell_volume   341.98149657
_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
  Ca  Ca0  1  0.03522100  0.52540400  0.97715800  1.0
  Ca  Ca1  1  0.86653100  0.86653100  0.40058200  1.0
  Ca  Ca2  1  0.35328600  0.35328600  0.50052000  1.0
  Ca  Ca3  1  0.52540400  0.03522100  0.97715800  1.0
  Sn  Sn4  1  0.09505600  0.09505600  0.22656200  1.0
  Sn  Sn5  1  0.41708100  0.80567600  0.52280800  1.0
  Sn  Sn6  1  0.80567600  0.41708100  0.52280800  1.0
  Sn  Sn7  1  0.56793900  0.56793900  0.14531500  1.0
  F  F8  1  0.33675500  0.33675500  0.84489400  1.0
  F  F9  1  0.56273300  0.15611900  0.25526100  1.0
  F  F10  1  0.15611900  0.56273300  0.25526100  1.0
  F  F11  1  0.04184200  0.63985500  0.65185200  1.0
  F  F12  1  0.63985500  0.04184200  0.65185200  1.0
  F  F13  1  0.82850200  0.82850200  0.06796700  1.0
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