Interpretable machine learning screening and analysis of pressure sensitivity and high-pressure emission line shifts in lanthanide- and transition-metal-doped phosphors
Source abstract
Abstract
An interpretable hierarchical machine learning framework is proposed to efficiently screen and analyse the absolute pressure sensitivity of lanthanide- and transition-metal-doped phosphors.
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short
Research context
OpenAlex topics: Luminescence Properties of Advanced Materials · Analytical Chemistry and Sensors · Luminescence and Fluorescent Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
Anhui Province Key Laboratory of Environment-Friendly Polymer Materials, Anhui University, Hefei 230601, China
Funding
National Natural Science Foundation of China
References (50)
Show source-provided references
1. Zou · APL Mater. · 2024 · 12 · 03060110.1063/5.0198102 ↗
2. Marciniak · Coord. Chem. Rev. · 2024 · 507 · 21577010.1016/j.ccr.2024.215770 ↗
3. Ma · J. Alloys Compd. · 2025 · 1044 · 18435010.1016/j.jallcom.2025.184350 ↗
4. Zheng · Chem. Eng. J. · 2022 · 443 · 13641410.1016/j.cej.2022.136414 ↗
5. Runowski · Mater. Horiz. · 2024 · 11 · 491110.1039/D4MH00871E ↗
6. Lou · Nano Res. · 2022 · 15 · 254510.1007/s12274-021-3736-x ↗
7. Li · Cell Rep. Phys. Sci. · 2025 · 6 · 10273310.1016/j.xcrp.2025.102733 ↗
8. Runowski · J. Mater. Chem. C · 2021 · 9 · 464310.1039/D1TC00709B ↗
9. Butler · Nature · 2018 · 559 · 54710.1038/s41586-018-0337-2 ↗
10. Ward · npj Comput. Mater. · 2016 · 2 · 1602810.1038/npjcompumats.2016.28 ↗
11. Dinic · Comput. Mater. Sci. · 2024 · 231 · 11258010.1016/j.commatsci.2023.112580 ↗
12. Park · Inorg. Chem. Front. · 2021 · 8 · 461010.1039/D1QI00766A ↗
13. Souza · J. Chem. Inf. Model. · 2025 · 65 · 327010.1021/acs.jcim.4c02403 ↗
14. Azies · Chem. Africa · 2024 · 7 · 466910.1007/s42250-024-01080-4 ↗
15. Marciniak · Coord. Chem. Rev. · 2024 · 507 · 21577010.1016/j.ccr.2024.215770 ↗
16. Chang · npj Comput. Mater. · 2022 · 8 · 24210.1038/s41524-022-00929-x ↗
17. Nikolic · Photonics · 2025 · 12 · 102410.3390/photonics12101024 ↗
18. Wang · Matter Radiat. Extremes · 2025 · 10 · 03380110.1063/5.0255385 ↗
19. Jin · Matter · 2024 · 7 · 1710.1016/j.matt.2023.11.019 ↗
20. Jin · J. Am. Chem. Soc. · 2025 · 147 · 796510.1021/jacs.5c00061 ↗
21. Wang · Phys. Rev. B · 2023 · 107 · 21411110.1103/PhysRevB.107.214111 ↗
22. Long · Ceram. Int. · 2025 · 51 · 1791210.1016/j.ceramint.2025.01.563 ↗
23. Shimul · J. Inorg. Organomet. Polym. · 2025 · 35 · 589010.1007/s10904-025-03629-3 ↗
24. Suchocki · J. Lumin. · 2007 · 125 · 26610.1016/j.jlumin.2006.08.039 ↗
25. García-Fernández · Chem. Phys. Lett. · 2006 · 426 · 9110.1016/j.cplett.2006.05.101 ↗
26. Forman · Science · 1972 · 176 · 28410.1126/science.176.4032.284 ↗
27. Piermarini · J. Appl. Phys. · 1975 · 46 · 277410.1063/1.321957 ↗
28. Mao · J. Appl. Phys. · 1978 · 49 · 327610.1063/1.325277 ↗
29. Mao · J. Geophys. Res.:Solid Earth · 1986 · 91 · 467310.1029/JB091iB05p04673 ↗
30. Grinberg · J. Lumin. · 2007 · 125 · 9710.1016/j.jlumin.2006.08.018 ↗
31. Back · Chem. Mater. · 2021 · 33 · 337910.1021/acs.chemmater.1c00678 ↗
32. Dorenbos · J. Lumin. · 2013 · 135 · 9310.1016/j.jlumin.2012.09.034 ↗
33. Ogutu · BMC Proc. · 2012 · 6 · S1010.1186/1753-6561-6-S2-S10 ↗
34. Hoerl · Technometrics · 1970 · 12 · 5510.1080/00401706.1970.10488634 ↗
35. Quinlan · Mach. Learn. · 1986 · 1 · 8110.1023/A:1022643204877 ↗
36. Breiman · Mach. Learn. · 2001 · 45 · 510.1023/A:1010933404324 ↗
37. Friedman · Ann. Stat. · 2001 · 29 · 118910.1214/aos/1013203451 ↗
38. T.Chen and C.Guestrin , XGBoost: A Scalable Tree Boosting System, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 16 (Association for Computing Machinery, 2016 ), pp. 785–794
39. G.Ke , Q.Meng , T.Finley , T.Wang , W.Chen , W.Ma , Q.Ye and T.-Y.Liu , LightGBM: A Highly Efficient Gradient Boosting Decision Tree, in Advances in Neural Information Processing Systems (Curran Associates, Inc., 2017 ), 30
40. Hancock · J. Big Data · 2020 · 7 · 9410.1186/s40537-020-00369-8 ↗
41. Tang · J. Nucl. Mater. · 2026 · 622 · 15641310.1016/j.jnucmat.2025.156413 ↗
42. Ma · J. Alloys Compd. · 2025 · 1044 · 18435010.1016/j.jallcom.2025.184350 ↗
43. Zheng · Adv. Funct. Mater. · 2023 · 33 · 221466310.1002/adfm.202214663 ↗
44. Szymczak · Chem. Eng. J. · 2023 · 466 · 14313010.1016/j.cej.2023.143130 ↗
45. Zheng · Chem. Eng. J. · 2022 · 446 · 13683910.1016/j.cej.2022.136839 ↗
46. Habibi · Eng. Appl. Artif. Intell. · 2023 · 118 · 10566910.1016/j.engappai.2022.105669 ↗
47. Maćkiewicz · Comput. Geosci. · 1993 · 19 · 30310.1016/0098-3004(93)90090-R ↗
48. Ahmed · Electronics · 2020 · 9 · 129510.3390/electronics9081295 ↗
49. Li · Comput. Environ. Urban Syst. · 2022 · 96 · 10184510.1016/j.compenvurbsys.2022.101845 ↗
50. Ranjan · Wireless Pers. Commun. · 2023 · 132 · 65110.1007/s11277-023-10628-5 ↗