journal article·2026-04-17Open access

Deciphering Short‐Range Order in 2D Transition Metal Dichalcogenides: From Origin to Multi‐Scale Property Modulation

Hanyu Liu, Linggang Zhu, Jian Zhou, Zhimei Sun

Abstract

ABSTRACT Chemical short‐range order (SRO) is a critical structural characteristic in multi‐principal element materials, governing the global electronic properties including band gap. Yet its effect on site‐resolved properties, such as magnetic moments and d‐band centers, remains unclear. Here, using equiatomic 2D ternary transition metal dichalcogenides (TMDCs) exemplified by (V 0.5 Cr 0.5 )S 2 and (Re 0.5 Ta 0.5 )S 2 , the origin and influence of SRO are unraveled via high‐throughput first‐principles calculations and machine learning. The results identify chemical affinity and atomic size difference as the dominant descriptors associated with SRO formation. Then, weak and strong SRO regimes are identified according to the energetic gain of SRO configurations relative to the quasi‐random states. Specifically, weak SRO in (V 0.5 Cr 0.5 )S 2 has negligible influence on its half‐metallic character, but significantly modulates the site‐resolved properties including atomic magnetic moments and d‐band centers. Further, the mapping between the local atomic arrangements of (V 0.5 Cr 0.5 )S 2 and the site‐resolved properties is accurately described by the many‐body descriptor MACE‐MP extracted from the universal interatomic potential. In contrast, strong SRO in (Re 0.5 Ta 0.5 )S 2 suppresses the localized mid‐gap states originating from Ta_dz 2 and Re_dz 2 /dx 2 ‐y 2 orbitals, leading to a semiconducting gap. These findings establish SRO as a fundamental degree of freedom for designing the multi‐scale functionalities of the materials.
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Research context

OpenAlex topics: 2D Materials and Applications · Machine Learning in Materials Science · Iron-based superconductors research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Materials Science and Engineering Beihang University Beijing China

Funding

National Natural Science Foundation of China

References (54)

Show source-provided references
1. 10.1016/j.pnsc.2016.05.00210.1016/j.pnsc.2016.05.002 ↗
2. 10.1038/natrevmats.2017.3310.1038/natrevmats.2017.33 ↗
3. 10.1038/s41524‐021‐00547‐z10.1038/s41524‐021‐00547‐z ↗
4. 10.1007/s40820‐017‐0152‐610.1007/s40820‐017‐0152‐6 ↗
5. 10.1002/adfm.20130076010.1002/adfm.201300760 ↗
6. 10.1038/ncomms188210.1038/ncomms1882 ↗
7. 10.1038/nnano.2013.15110.1038/nnano.2013.151 ↗
8. 10.1038/ncomms667810.1038/ncomms6678 ↗
9. 10.1002/adma.20150430910.1002/adma.201504309 ↗
10. 10.1002/adma.20160285410.1002/adma.201602854 ↗
11. 10.1021/acscatal.2c0482510.1021/acscatal.2c04825 ↗
12. 10.1021/jacs.4c1386310.1021/jacs.4c13863 ↗
13. 10.1002/adma.20220552410.1002/adma.202205524 ↗
14. 10.1002/adfm.20240675510.1002/adfm.202406755 ↗
15. 10.1021/acsnano.2c0411310.1021/acsnano.2c04113 ↗
16. 10.1021/acsami.0c1310410.1021/acsami.0c13104 ↗
17. 10.1002/mgea.710.1002/mgea.7 ↗
18. 10.1039/C9TC05487A10.1039/C9TC05487A ↗
19. 10.1002/adem.20030056710.1002/adem.200300567 ↗
20. 10.1016/j.msea.2003.10.25710.1016/j.msea.2003.10.257 ↗
21. 10.1103/PhysRevB.86.24520210.1103/PhysRevB.86.245202 ↗
22. 10.1103/PhysRevLett.61.162710.1103/PhysRevLett.61.1627 ↗
23. 10.1126/sciadv.170027010.1126/sciadv.1700270 ↗
24. 10.1038/s41563‐024‐01912‐110.1038/s41563‐024‐01912‐1 ↗
25. 10.1051/jphysrad:01962002301007490110.1051/jphysrad:019620023010074901 ↗
26. 10.1016/j.actamat.2021.11693810.1016/j.actamat.2021.116938 ↗
27. 10.1073/pnas.180866011510.1073/pnas.1808660115 ↗
28. 10.1016/j.jmst.2024.11.06310.1016/j.jmst.2024.11.063 ↗
29. 10.1038/s41467‐021‐25264‐510.1038/s41467‐021‐25264‐5 ↗
30. 10.1021/acs.nanolett.6b0307510.1021/acs.nanolett.6b03075 ↗
31. 10.1103/PhysRevMaterials.5.02460410.1103/PhysRevMaterials.5.024604 ↗
32. 10.1103/PhysRevLett.124.09610110.1103/PhysRevLett.124.096101 ↗
33. 10.1038/s41524‐025‐01899‐610.1038/s41524‐025‐01899‐6 ↗
34. 10.1021/acs.chemmater.7b0452710.1021/acs.chemmater.7b04527 ↗
35. 10.1103/PhysRev.77.66910.1103/PhysRev.77.669 ↗
36. 10.1002/adma.20190704110.1002/adma.201907041 ↗
37. 10.1016/j.actamat.2021.11663810.1016/j.actamat.2021.116638 ↗
38. 10.1007/s11661‐013‐2000‐810.1007/s11661‐013‐2000‐8 ↗
39. 10.1016/j.actamat.2025.12072610.1016/j.actamat.2025.120726 ↗
40. 10.1021/acs.jpclett.2c0297210.1021/acs.jpclett.2c02972 ↗
41. 10.1103/PhysRevB.87.18411510.1103/PhysRevB.87.184115 ↗
42. 10.1063/5.015103110.1063/5.0151031 ↗
43. 10.1063/5.029700610.1063/5.0297006 ↗
44. 10.48550/arXiv.2302.1423110.48550/arXiv.2302.14231 ↗
45. 10.1002/adfm.20210765110.1002/adfm.202107651 ↗
46. 10.1038/s41524‐025‐01777‐110.1038/s41524‐025‐01777‐1 ↗
47. 10.1103/PhysRevB.54.1028410.1103/PhysRevB.54.10284 ↗
48. 10.1016/0927‐0256(96)00008‐010.1016/0927‐0256(96)00008‐0 ↗
49. 10.1103/PhysRevB.54.1116910.1103/PhysRevB.54.11169 ↗
50. 10.1103/PhysRevLett.77.386510.1103/PhysRevLett.77.3865 ↗
51. 10.1103/PhysRevB.57.150510.1103/PhysRevB.57.1505 ↗
52. 10.1103/PhysRevB.105.11511510.1103/PhysRevB.105.115115 ↗
53. 10.1002/adfm.20240675510.1002/adfm.202406755 ↗
54. F.Pedregosa F.Pedregosa G.Varoquaux et al. Scikit‐learn: Machine Learning in Python.