journal article·2025-10-16Open access

Obtaining highly transferable parameters for empirical tight-binding simulations: From pure materials to polytypic heterostructures and dilute alloys

Anh-Luan Phan, Daniele Soccodato, Alessandro Pecchia, Alessia Di Vito, Matthias Auf der Maur

Physical Review B· Volume 112· Issue 16

Abstract

The tedious parametrization process remains a key bottleneck hindering broader adoption of the empirical tight-binding method. Here, we discuss the challenges and requirements of finding parameters for Slater-Koster-based empirical tight-binding schemes, and explain the shortcomings of the parametrization procedures commonly used in the literature. We then propose our way to address these challenges, through the presentation of an open-sourced parametrization toolbox that breaks up the task into several, incremental stages. The prototype of our toolbox is tailored especially to deal with a recently proposed, highly transferable scheme, but can be easily adapted to any Slater-Koster-based scheme. A case study of fitting GaN parameters is considered. Remarkably, the obtained GaN parameter set shows high transferability when applied to various contexts: from simple zincblende or wurtzite bulk cases, to polytypic heterostructures and even highly mismatched alloys in their dilute limits. This confirms the effectiveness and robustness of our parametrization approach.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Machine Learning in Materials Science · 2D Materials and Applications · Advanced Chemical Physics Studies

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

Affiliations

University of Rome Tor Vergata

CNR-ISMN

Funding

Horizon 2020

References (87)

Show source-provided references
1. 10.1103/PhysRev.94.149810.1103/PhysRev.94.1498 ↗
2. 10.1088/0034-4885/60/12/00110.1088/0034-4885/60/12/001 ↗
3. 10.1088/0268-1242/18/1/20110.1088/0268-1242/18/1/201 ↗
4. 10.1088/0953-8984/15/10/20110.1088/0953-8984/15/10/201 ↗
5. A. T. Paxton · 2009
6. G. Klimeck · 2023
7. 10.1103/PhysRevB.86.11542410.1103/PhysRevB.86.115424 ↗
8. 10.1103/PhysRevB.74.20532310.1103/PhysRevB.74.205323 ↗
9. 10.1103/PhysRevLett.116.02740110.1103/PhysRevLett.116.027401 ↗
10. 10.1088/1402-4896/ad4f6510.1088/1402-4896/ad4f65 ↗
11. 10.1016/j.commatsci.2021.11053510.1016/j.commatsci.2021.110535 ↗
12. 10.1103/RevModPhys.84.141910.1103/RevModPhys.84.1419 ↗
13. 10.1103/PhysRevB.111.12520310.1103/PhysRevB.111.125203 ↗
14. 10.1016/0022-3697(83)90064-110.1016/0022-3697(83)90064-1 ↗
15. 10.1103/PhysRevB.57.649310.1103/PhysRevB.57.6493 ↗
16. O. Madelung · 200410.1007/978-3-642-18865-7 ↗
17. 10.1063/1.174763210.1063/1.1747632 ↗
18. G. L. Bir · 1974
19. 10.1103/PhysRevB.88.21410310.1103/PhysRevB.88.214103 ↗
20. 10.1103/PhysRevB.94.04531110.1103/PhysRevB.94.045311 ↗
21. 10.1103/PhysRevB.76.11520210.1103/PhysRevB.76.115202 ↗
22. 10.1063/1.152931210.1063/1.1529312 ↗
23. 10.1088/0953-8984/28/4/04500110.1088/0953-8984/28/4/045001 ↗
24. 10.1088/0953-8984/28/30/30580110.1088/0953-8984/28/30/305801 ↗
25. 10.1103/PhysRevB.66.12520710.1103/PhysRevB.66.125207 ↗
26. 10.1103/PhysRevB.79.24520110.1103/PhysRevB.79.245201 ↗
27. 10.1103/PhysRevB.81.12520210.1103/PhysRevB.81.125202 ↗
28. 10.1016/j.commatsci.2022.11147910.1016/j.commatsci.2022.111479 ↗
29. 10.1007/s10825-013-0436-010.1007/s10825-013-0436-0 ↗
30. 10.1103/PhysRevB.92.08530110.1103/PhysRevB.92.085301 ↗
31. To compare the wavefunctions, the DFT wavefunctions are projected onto the orthogonalized atomic-like basis set to obtain the DFT expansion coefficients. Then those expansion coefficients will be compared with the ETB ones.
32. https://commons.wikimedia.org/w/index.php?curid=2146789.
33. Gamultiobj algorithm - MATLAB & simulink, https://www.mathworks.com/help/gads/gamultiobj-algorithm.html.
34. The MOGA of MATLAB®is much more convenient, robust and efficient than an user-implemented version. The parallelization to speed up the algorithm can also be enabled easily.
35. 10.1088/1361-648X/ab400710.1088/1361-648X/ab4007 ↗
36. 10.1016/j.spmi.2007.03.01110.1016/j.spmi.2007.03.011 ↗
37. 10.1007/s11082-009-9272-710.1007/s11082-009-9272-7 ↗
38. We chose to use the “Fitted” columns, but one can use “Analytical” column without much differences, noting that this is just for rough estimations.
39. 10.1515/crll.1909.136.21010.1515/crll.1909.136.210 ↗
40. 10.1063/1.154111310.1063/1.1541113 ↗
41. 10.1088/0953-8984/22/29/29530410.1088/0953-8984/22/29/295304 ↗
42. 10.1063/5.012900710.1063/5.0129007 ↗
43. 10.1021/acs.nanolett.5b0025310.1021/acs.nanolett.5b00253 ↗
44. 10.1088/1367-2630/18/6/06300910.1088/1367-2630/18/6/063009 ↗
45. 10.1088/1361-6641/ab0cb810.1088/1361-6641/ab0cb8 ↗
46. 10.1007/s12274-012-0232-310.1007/s12274-012-0232-3 ↗
47. 10.1103/PhysRevB.108.07510410.1103/PhysRevB.108.075104 ↗
48. L. S. Ramsdell · Am. Mineral. · 1947 · 32 · 64
49. 10.1002/pssb.222054021110.1002/pssb.2220540211 ↗
50. 10.1103/PhysRevB.49.1622310.1103/PhysRevB.49.16223 ↗
51. 10.1063/5.002537110.1063/5.0025371 ↗
52. 10.1063/1.325943410.1063/1.3259434 ↗
53. 10.1063/1.489636410.1063/1.4896364 ↗
54. 10.1063/1.514224810.1063/1.5142248 ↗
55. 10.1088/0268-1242/27/9/09401110.1088/0268-1242/27/9/094011 ↗
56. 10.1103/PhysRevLett.82.122110.1103/PhysRevLett.82.1221 ↗
57. 10.1002/1521-3951(200101)223:1%3C75::AID-PSSB75%3E3.0.CO;2-110.1002/1521-3951(200101)223:1%3C75::AID-PSSB75%3E3.0.CO;2-1 ↗
58. 10.1088/0268-1242/17/8/31510.1088/0268-1242/17/8/315 ↗
59. 10.1063/1.12659710.1063/1.126597 ↗
60. 10.1103/PhysRevB.65.24130310.1103/PhysRevB.65.241303 ↗
61. 10.1063/1.234983210.1063/1.2349832 ↗
62. 10.1063/1.509797710.1063/1.5097977 ↗
63. 10.1063/5.017925510.1063/5.0179255 ↗
64. 10.1103/PhysRevApplied.23.02400510.1103/PhysRevApplied.23.024005 ↗
65. 10.1103/PhysRevB.85.08513410.1103/PhysRevB.85.085134 ↗
66. 10.1103/PhysRevB.70.08520910.1103/PhysRevB.70.085209 ↗
67. 10.1063/1.149446910.1063/1.1494469 ↗
68. 10.1063/1.205822410.1063/1.2058224 ↗
69. 10.1063/1.147640010.1063/1.1476400 ↗
70. 10.1016/S0022-0248(02)01079-510.1016/S0022-0248(02)01079-5 ↗
71. 10.1103/PhysRevB.70.11521410.1103/PhysRevB.70.115214 ↗
72. 10.1063/1.493259210.1063/1.4932592 ↗
73. 10.1103/PhysRevB.105.08530710.1103/PhysRevB.105.085307 ↗
74. 10.1088/2632-2153/ad451010.1088/2632-2153/ad4510 ↗
75. 10.1063/5.019204710.1063/5.0192047 ↗
76. 10.1103/PhysRevB.48.283910.1103/PhysRevB.48.2839 ↗
77. 10.1103/PhysRevB.47.710410.1103/PhysRevB.47.7104 ↗
78. 10.1103/PhysRevB.49.850610.1103/PhysRevB.49.8506 ↗
79. 10.1103/PhysRevB.51.1677210.1103/PhysRevB.51.16772 ↗
80. 10.1088/0953-8984/11/8/01710.1088/0953-8984/11/8/017 ↗
81. 10.1063/1.156406010.1063/1.1564060 ↗
82. 10.1016/j.cpc.2018.01.01210.1016/j.cpc.2018.01.012 ↗
83. 10.1016/j.physe.2009.11.03510.1016/j.physe.2009.11.035 ↗
84. 10.1063/1.136815610.1063/1.1368156 ↗
85. 10.1063/1.160051910.1063/1.1600519 ↗
86. 10.1103/PhysRevLett.95.21550510.1103/PhysRevLett.95.215505 ↗
87. 10.1063/5.013210910.1063/5.0132109 ↗