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MgZnRh2

高熵材料 HEA 传统材料 TRA 弹性数据 ELA

MP-ID: mp-977547 · 空间群: Fm-3m (#225)

基础信息

化学式
MgZnRh2
MP-ID
mp-977547
元素数
3
位点数
4
密度
8.7776
g/cm³
体积
55.91
ų
晶胞参数 a
4.2920
Å
晶胞参数 b
4.2920
Å
晶胞参数 c
4.2920
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
是
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 9)
2θd (Å)Ihkl
25.42 3.5044 5.6 (1,1,1)
29.43 3.0349 18.4 (1,1,0)
42.11 2.1460 100.0 (2,1,1)
49.83 1.8301 2.7 (2,2,1)
52.20 1.7522 4.9 (2,2,2)
61.07 1.5175 16.1 (2,2,0)
67.23 1.3925 1.0 (3,2,2)
69.22 1.3573 6.3 (3,2,1)
76.96 1.2390 31.9 (3,3,2)
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能量属性

形成能/原子
-0.42482
eV/atom
能量/原子
-4.82953
eV/atom
凸包距离
0.0000
eV/atom
未修正能量
-15.7810
eV/atom
体积/原子
13.98
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
0.0000
μB
磁有序
Unknown
磁性位点数
0
磁化强度/体积
0.000000
μB/ų

弹性属性

体积模量 KVRH
140.599
GPa
剪切模量 GVRH
62.854
GPa
杨氏模量 E
164.108
GPa
泊松比 ν
0.305
Pugh 比 K/G
2.237
维氏硬度估算
5.790
GPa
韧性
Brittle
各向异性
0.753
拉梅 λ
98.696
GPa

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
Rh · 0.500
元素家族
4d-TM
溶质分数
0.500
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
1.0397
高熵判据(>1.0 视为高熵)
混合熵 ΔS
8.6442
J/mol·K
Ω 参数
0.5014
Ω > 1.1 利于固溶体形成
原子尺寸错配 δr
8.013%
δr < 6.6% 利于单相固溶
电负性差 Δχ
0.4177
越小混合越稳定
价电子浓度 VEC
8.000
价电子浓度,影响相稳定性
混合焓 ΔH_mix
-26.248
kJ/mol 且负值为放热混合
熵归一化
0.9464
固溶体倾向
0
相趋势
AM

传统材料特征

ROM 混合特征雷达
评级与分类
评级 Tier
B
评分
0.509
家族
Rh-rich Multi-Component
基质元素
Rh · 0.500

弹性数据特征

弹性模量对比 (GPa)
弹性特征
弹性各向异性 AU
0.7530
均匀泊松比 ν
0.3050
Debye 温度
369.6
K
声速
2,991.1
m/s
均匀度
0.3050

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-977547
参考引用

The crystal structure and related material properties of MgZnRh2 were obtained from the Materials Project database (MP-ID: mp-977547, 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_MgZnRh2
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   4.29200684
_cell_length_b   4.29200684
_cell_length_c   4.29200684
_cell_angle_alpha   60.00000000
_cell_angle_beta   60.00000000
_cell_angle_gamma   60.00000000
_symmetry_Int_Tables_number   1
_chemical_formula_structural   MgZnRh2
_chemical_formula_sum   'Mg1 Zn1 Rh2'
_cell_volume   55.90700378
_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
  Mg  Mg0  1  0.24999900  0.24999900  0.24999900  1.0
  Zn  Zn1  1  0.74999900  0.74999900  0.74999900  1.0
  Rh  Rh2  1  0.00000000  -0.00000000  -0.00000000  1.0
  Rh  Rh3  1  0.50000000  0.50000000  0.50000000  1.0
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