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Er2Re2Si2C

高熵材料 HEA

MP-ID: mp-1025362 · 空间群: C2/m (#12)

基础信息

化学式
Er2Re2Si2C
MP-ID
mp-1025362
元素数
4
位点数
7
密度
10.8622
g/cm³
体积
118.49
ų
晶胞参数 a
5.8003
Å
晶胞参数 b
5.8003
Å
晶胞参数 c
6.8846
Å
α 角
55.19
°
β 角
55.19
°
γ 角
40.11
°
空间群
C2/m
点群
2/m
晶体系统
Monoclinic
布拉维格子
Monoclinic C
稳定性
是
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
16.21 5.4672 24.8 (0,0,1)
20.53 4.3269 4.9 (1,1,0)
24.63 3.6143 11.7 (1,0,0)
25.87 3.4439 15.2 (1,0,1)
25.89 3.4407 9.1 (1,1,2)
32.76 2.7336 3.0 (0,0,2)
33.00 2.7146 5.2 (1,0,-1)
33.31 2.6894 44.2 (1,1,-1)
33.50 2.6749 95.9 (2,1,1)
33.77 2.6543 12.1 (2,2,2)
34.06 2.6321 14.0 (2,2,1)
35.80 2.5079 100.0 (1,0,2)
38.55 2.3352 22.4 (2,1,0)
40.37 2.2340 4.9 (1,1,3)
41.01 2.2008 28.9 (2,2,3)
45.61 1.9889 29.2 (1,-1,0)
46.46 1.9546 3.0 (1,0,-2)
47.59 1.9106 2.5 (3,2,2)
48.72 1.8691 3.0 (1,-1,1)
49.06 1.8569 4.6 (1,1,-2)
49.41 1.8446 7.4 (2,1,-1)
50.00 1.8240 4.2 (1,0,3)
50.05 1.8224 2.4 (0,0,3)
50.51 1.8070 8.0 (3,3,2)
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能量属性

形成能/原子
-0.61697
eV/atom
能量/原子
-8.34744
eV/atom
凸包距离
0.0000
eV/atom
未修正能量
-8.3474
eV/atom
体积/原子
16.93
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
0.0001
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
Er · 0.286
元素家族
Lanthanide
溶质分数
0.714
间隙分数
0.429

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
1.3518
高熵判据(>1.0 视为高熵)
混合熵 ΔS
11.2387
J/mol·K
Ω 参数
2.2945
Ω > 1.1 利于固溶体形成
原子尺寸错配 δr
24.020%
δr < 6.6% 利于单相固溶
电负性差 Δχ
0.4191
越小混合越稳定
价电子浓度 VEC
7.143
价电子浓度,影响相稳定性
混合焓 ΔH_mix
-12.399
kJ/mol 且负值为放热混合
熵归一化
0.9751
固溶体倾向
0
相趋势
IM

关联任务

引用参考

Materials Project 官方页面
https://materialsproject.org/materials/mp-1025362
参考引用

The crystal structure and related material properties of Er2Re2Si2C were obtained from the Materials Project database (MP-ID: mp-1025362, 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_Er2Re2Si2C
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   5.80028357
_cell_length_b   5.80028357
_cell_length_c   6.88460156
_cell_angle_alpha   55.18589995
_cell_angle_beta   55.18589995
_cell_angle_gamma   40.10745538
_symmetry_Int_Tables_number   1
_chemical_formula_structural   Er2Re2Si2C
_chemical_formula_sum   'Er2 Re2 Si2 C1'
_cell_volume   118.49464187
_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
  Er  Er0  1  0.42801900  0.42801900  0.29879700  1.0
  Er  Er1  1  0.57198100  0.57198100  0.70120300  1.0
  Re  Re2  1  0.79234400  0.79234400  0.09392300  1.0
  Re  Re3  1  0.20765600  0.20765600  0.90607700  1.0
  Si  Si4  1  0.85872400  0.85872400  0.67912600  1.0
  Si  Si5  1  0.14127600  0.14127600  0.32087400  1.0
  C  C6  1  0.00000000  0.00000000  0.00000000  1.0
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