Deciphering the Key Factors Governing Mn<sup>4+</sup> Zero-Phonon Line Characteristics via Machine Learning Decoding of Host–Mn<sup>4+</sup> Interactions
Achieving wide color gamut in liquid crystal displays relies critically on narrow-band red-emitting phosphors. Mn<sup>4+</sup>-activated phosphors are promising candidates due to their sharp emission, yet modulating their zero-phonon line wavelengths remains challenging. This study emplo...
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2025
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| _version_ | 1851482227454509056 |
|---|---|
| author | Jinxin Wang (1696837) |
| author2 | Yuanyuan Dou (8472507) Jiahua Zhang (567192) Mingyue Chen (1408384) Zhen Song (412533) Quanlin Liu (1412533) |
| author2_role | author author author author author |
| author_facet | Jinxin Wang (1696837) Yuanyuan Dou (8472507) Jiahua Zhang (567192) Mingyue Chen (1408384) Zhen Song (412533) Quanlin Liu (1412533) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jinxin Wang (1696837) Yuanyuan Dou (8472507) Jiahua Zhang (567192) Mingyue Chen (1408384) Zhen Song (412533) Quanlin Liu (1412533) |
| dc.date.none.fl_str_mv | 2025-10-08T03:43:19Z |
| dc.identifier.none.fl_str_mv | 10.1021/acs.jpcc.5c05715.s002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Deciphering_the_Key_Factors_Governing_Mn_sup_4_sup_Zero-Phonon_Line_Characteristics_via_Machine_Learning_Decoding_of_Host_Mn_sup_4_sup_Interactions/30302942 |
| dc.rights.none.fl_str_mv | CC BY-NC 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Microbiology Cell Biology Neuroscience Biotechnology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified related parameters dominate promising candidates due cumulative importance ), also contribute significantly 928 ), revealing 17 oxides ). emission peak wavelengths 2 </ sup g </ sub 2g </ sub host – mn band red emitters 4 +</ sup 4 </ sup band red r </ sharp emission >< sup yet modulating transition energy test mae targeted strategy oxyfluoride phosphors geometric factors emitting phosphors discovering next bond angles 6 fluoroxides 42 fluorides 133 nm |
| dc.title.none.fl_str_mv | Deciphering the Key Factors Governing Mn<sup>4+</sup> Zero-Phonon Line Characteristics via Machine Learning Decoding of Host–Mn<sup>4+</sup> Interactions |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Achieving wide color gamut in liquid crystal displays relies critically on narrow-band red-emitting phosphors. Mn<sup>4+</sup>-activated phosphors are promising candidates due to their sharp emission, yet modulating their zero-phonon line wavelengths remains challenging. This study employs machine learning to decode host–Mn<sup>4+</sup> interactions across 65 distinct hosts (42 fluorides, 6 fluoroxides, 17 oxides). By extracting 29 structural descriptors and leveraging a random forest regression model, we identify nine key features governing ZPL wavelengths. Electronegativity-related parameters dominate (77.83% cumulative importance), while geometric factors (bond angles, distances) also contribute significantly. The model achieves high accuracy (test MAE = 4.133 nm, <i>R</i><sup>2</sup> = 0.928), revealing that high electronegativity in secondary-coordination ions enhances Mn–ligand covalency, reducing the E<sub>g</sub> → <sup>4</sup>A<sub>2g</sub> transition energy and redshifting the emission peak wavelengths. This work identifies key design principles for Mn<sup>4+</sup>-activated fluoride, oxide, and oxyfluoride phosphors, enabling a targeted strategy for discovering next-generation narrow-band red emitters. |
| eu_rights_str_mv | openAccess |
| id | Manara_dcbbbb80cd9d7e2ccc863f94e3cc6ec1 |
| identifier_str_mv | 10.1021/acs.jpcc.5c05715.s002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30302942 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY-NC 4.0 |
| spelling | Deciphering the Key Factors Governing Mn<sup>4+</sup> Zero-Phonon Line Characteristics via Machine Learning Decoding of Host–Mn<sup>4+</sup> InteractionsJinxin Wang (1696837)Yuanyuan Dou (8472507)Jiahua Zhang (567192)Mingyue Chen (1408384)Zhen Song (412533)Quanlin Liu (1412533)BiochemistryMicrobiologyCell BiologyNeuroscienceBiotechnologyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedrelated parameters dominatepromising candidates duecumulative importance ),also contribute significantly928 ), revealing17 oxides ).emission peak wavelengths2 </ supg </ sub2g </ subhost – mnband red emitters4 +</ sup4 </ supband redr </sharp emission>< supyet modulatingtransition energytest maetargeted strategyoxyfluoride phosphorsgeometric factorsemitting phosphorsdiscovering nextbond angles6 fluoroxides42 fluorides133 nmAchieving wide color gamut in liquid crystal displays relies critically on narrow-band red-emitting phosphors. Mn<sup>4+</sup>-activated phosphors are promising candidates due to their sharp emission, yet modulating their zero-phonon line wavelengths remains challenging. This study employs machine learning to decode host–Mn<sup>4+</sup> interactions across 65 distinct hosts (42 fluorides, 6 fluoroxides, 17 oxides). By extracting 29 structural descriptors and leveraging a random forest regression model, we identify nine key features governing ZPL wavelengths. Electronegativity-related parameters dominate (77.83% cumulative importance), while geometric factors (bond angles, distances) also contribute significantly. The model achieves high accuracy (test MAE = 4.133 nm, <i>R</i><sup>2</sup> = 0.928), revealing that high electronegativity in secondary-coordination ions enhances Mn–ligand covalency, reducing the E<sub>g</sub> → <sup>4</sup>A<sub>2g</sub> transition energy and redshifting the emission peak wavelengths. This work identifies key design principles for Mn<sup>4+</sup>-activated fluoride, oxide, and oxyfluoride phosphors, enabling a targeted strategy for discovering next-generation narrow-band red emitters.2025-10-08T03:43:19ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1021/acs.jpcc.5c05715.s002https://figshare.com/articles/dataset/Deciphering_the_Key_Factors_Governing_Mn_sup_4_sup_Zero-Phonon_Line_Characteristics_via_Machine_Learning_Decoding_of_Host_Mn_sup_4_sup_Interactions/30302942CC BY-NC 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303029422025-10-08T03:43:19Z |
| spellingShingle | Deciphering the Key Factors Governing Mn<sup>4+</sup> Zero-Phonon Line Characteristics via Machine Learning Decoding of Host–Mn<sup>4+</sup> Interactions Jinxin Wang (1696837) Biochemistry Microbiology Cell Biology Neuroscience Biotechnology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified related parameters dominate promising candidates due cumulative importance ), also contribute significantly 928 ), revealing 17 oxides ). emission peak wavelengths 2 </ sup g </ sub 2g </ sub host – mn band red emitters 4 +</ sup 4 </ sup band red r </ sharp emission >< sup yet modulating transition energy test mae targeted strategy oxyfluoride phosphors geometric factors emitting phosphors discovering next bond angles 6 fluoroxides 42 fluorides 133 nm |
| status_str | publishedVersion |
| title | Deciphering the Key Factors Governing Mn<sup>4+</sup> Zero-Phonon Line Characteristics via Machine Learning Decoding of Host–Mn<sup>4+</sup> Interactions |
| title_full | Deciphering the Key Factors Governing Mn<sup>4+</sup> Zero-Phonon Line Characteristics via Machine Learning Decoding of Host–Mn<sup>4+</sup> Interactions |
| title_fullStr | Deciphering the Key Factors Governing Mn<sup>4+</sup> Zero-Phonon Line Characteristics via Machine Learning Decoding of Host–Mn<sup>4+</sup> Interactions |
| title_full_unstemmed | Deciphering the Key Factors Governing Mn<sup>4+</sup> Zero-Phonon Line Characteristics via Machine Learning Decoding of Host–Mn<sup>4+</sup> Interactions |
| title_short | Deciphering the Key Factors Governing Mn<sup>4+</sup> Zero-Phonon Line Characteristics via Machine Learning Decoding of Host–Mn<sup>4+</sup> Interactions |
| title_sort | Deciphering the Key Factors Governing Mn<sup>4+</sup> Zero-Phonon Line Characteristics via Machine Learning Decoding of Host–Mn<sup>4+</sup> Interactions |
| topic | Biochemistry Microbiology Cell Biology Neuroscience Biotechnology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified related parameters dominate promising candidates due cumulative importance ), also contribute significantly 928 ), revealing 17 oxides ). emission peak wavelengths 2 </ sup g </ sub 2g </ sub host – mn band red emitters 4 +</ sup 4 </ sup band red r </ sharp emission >< sup yet modulating transition energy test mae targeted strategy oxyfluoride phosphors geometric factors emitting phosphors discovering next bond angles 6 fluoroxides 42 fluorides 133 nm |