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TlC4S4N

高熵材料 HEA

MP-ID: mp-1189292 · 空间群: P-1 (#2)

基础信息

化学式
TlC4S4N
MP-ID
mp-1189292
元素数
4
位点数
20
密度
2.4633
g/cm³
体积
532.14
ų
晶胞参数 a
6.7843
Å
晶胞参数 b
9.0168
Å
晶胞参数 c
9.9683
Å
α 角
111.26
°
β 角
93.91
°
γ 角
107.23
°
空间群
P-1
点群
-1
晶体系统
Triclinic
布拉维格子
Triclinic P
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
9.71 9.1079 83.8 (0,0,1)
11.22 7.8870 43.4 (0,1,0)
11.52 7.6811 100.0 (0,1,-1)
13.94 6.3525 4.0 (1,0,0)
14.58 6.0770 1.6 (1,-1,0)
15.35 5.7716 4.5 (1,0,-1)
16.53 5.3631 3.9 (1,-1,1)
18.17 4.8834 31.0 (0,1,-2)
18.51 4.7946 1.1 (1,-1,-1)
18.54 4.7864 4.5 (1,0,1)
19.59 4.5318 9.9 (1,1,-1)
20.76 4.2783 5.2 (1,1,0)
21.41 4.1510 4.2 (1,-2,1)
21.65 4.1041 5.5 (1,0,-2)
22.05 4.0315 26.2 (1,-2,0)
22.55 3.9435 6.6 (0,2,0)
22.95 3.8759 11.6 (1,-1,2)
23.00 3.8674 6.2 (1,1,-2)
23.16 3.8406 1.7 (0,2,-2)
25.83 3.4497 16.6 (1,-1,-2)
25.93 3.4363 23.7 (1,1,1)
26.23 3.3975 12.6 (1,0,2)
26.23 3.3970 2.4 (0,1,2)
26.39 3.3779 23.7 (2,-1,0)
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能量属性

形成能/原子
0.66661
eV/atom
能量/原子
-5.75172
eV/atom
凸包距离
0.9063
eV/atom
未修正能量
-5.5144
eV/atom
体积/原子
26.61
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
1.1834
μB
磁有序
NM
磁性位点数
0
磁化强度/体积
0.002220
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
C · 0.400
元素家族
Metalloid-Nonmetal
溶质分数
0.600
间隙分数
0.500

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
1.1936
高熵判据(>1.0 视为高熵)
混合熵 ΔS
9.9232
J/mol·K
Ω 参数
2.4722
Ω > 1.1 利于固溶体形成
原子尺寸错配 δr
28.482%
δr < 6.6% 利于单相固溶
电负性差 Δχ
0.3314
越小混合越稳定
价电子浓度 VEC
4.800
价电子浓度,影响相稳定性
混合焓 ΔH_mix
7.019
kJ/mol 且负值为放热混合
熵归一化
0.8610
固溶体倾向
0
相趋势
IM

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-1189292
参考引用

The crystal structure and related material properties of TlC4S4N were obtained from the Materials Project database (MP-ID: mp-1189292, 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_TlC4S4N
_symmetry_space_group_name_H-M   'P 1'
_cell_length_a   6.78425700
_cell_length_b   9.01674608
_cell_length_c   9.96828730
_cell_angle_alpha   111.25504367
_cell_angle_beta   93.90500669
_cell_angle_gamma   107.23214855
_symmetry_Int_Tables_number   1
_chemical_formula_structural   TlC4S4N
_chemical_formula_sum   'Tl2 C8 S8 N2'
_cell_volume   532.13615588
_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
  Tl  Tl0  1  0.16240400  0.85707700  0.41946100  1.0
  Tl  Tl1  1  0.83759600  0.14292300  0.58053900  1.0
  C  C2  1  0.03785000  0.77643000  0.74553900  1.0
  C  C3  1  0.96215000  0.22357000  0.25446100  1.0
  C  C4  1  0.06733500  0.72907400  0.89358500  1.0
  C  C5  1  0.93266500  0.27092600  0.10641500  1.0
  C  C6  1  0.15337200  0.87485500  0.68777700  1.0
  C  C7  1  0.84662800  0.12514500  0.31222300  1.0
  C  C8  1  0.86453900  0.65198800  0.74705300  1.0
  C  C9  1  0.13546100  0.34801200  0.25294700  1.0
  S  S10  1  0.59071900  0.98286000  0.22526700  1.0
  S  S11  1  0.40928100  0.01714000  0.77473300  1.0
  S  S12  1  0.57508100  0.76602100  0.44388300  1.0
  S  S13  1  0.42491900  0.23397900  0.55611700  1.0
  S  S14  1  0.62681000  0.76352000  0.10747900  1.0
  S  S15  1  0.37319000  0.23648000  0.89252100  1.0
  S  S16  1  0.67720300  0.65261400  0.26873800  1.0
  S  S17  1  0.32279700  0.34738600  0.73126200  1.0
  N  N18  1  0.90373700  0.59969200  0.86712800  1.0
  N  N19  1  0.09626300  0.40030800  0.13287200  1.0
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