A Practical Application of Machine Learning for the Development of Metallole-Based Fluorescent Materials
We have built a prediction model of the fluorescence quantum yields of metalloles. Based on the suggestion by the prediction model, we synthesized 10 fluorescent molecules to confirm the prediction accuracy. By measuring the fluorescence quantum yields of the synthesized molecules, it was demonstrat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/30/8/1686 |
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| author | Yusuke Kanematsu Akiyoshi Ohta Shunya Nagai Yohei Adachi Hiromasa Kaneko Takayoshi Ishimoto Takio Kurita Joji Ohshita |
| author_facet | Yusuke Kanematsu Akiyoshi Ohta Shunya Nagai Yohei Adachi Hiromasa Kaneko Takayoshi Ishimoto Takio Kurita Joji Ohshita |
| author_sort | Yusuke Kanematsu |
| collection | DOAJ |
| description | We have built a prediction model of the fluorescence quantum yields of metalloles. Based on the suggestion by the prediction model, we synthesized 10 fluorescent molecules to confirm the prediction accuracy. By measuring the fluorescence quantum yields of the synthesized molecules, it was demonstrated that our prediction model reasonably classified the quantum yields with an accuracy of 0.7. In particular, the low quantum yields were perfectly predicted for the synthesized molecules, demonstrating the usefulness of our prediction model to screen out weakly fluorescent molecules from the candidates. On the other hand, the low precision of 0.5 was attributed to the bias in the training dataset containing many fluorine-containing molecules with high quantum yields. Our prediction model was then revised with the generator of candidate molecular structures for more efficient development of fluorescent materials with taking the applicability domain into account, and the improvement of the applicability was confirmed owing to the increment of the dataset. |
| format | Article |
| id | doaj-art-d51f615b6839438b9d65671568bca12e |
| institution | DOAJ |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj-art-d51f615b6839438b9d65671568bca12e2025-08-20T03:13:50ZengMDPI AGMolecules1420-30492025-04-01308168610.3390/molecules30081686A Practical Application of Machine Learning for the Development of Metallole-Based Fluorescent MaterialsYusuke Kanematsu0Akiyoshi Ohta1Shunya Nagai2Yohei Adachi3Hiromasa Kaneko4Takayoshi Ishimoto5Takio Kurita6Joji Ohshita7Smart Innovation Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, JapanSmart Innovation Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, JapanSmart Innovation Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, JapanSmart Innovation Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, JapanSmart Innovation Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, JapanSmart Innovation Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, Japan(Professor Emeritus) Informatics and Data Science Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, JapanSmart Innovation Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, JapanWe have built a prediction model of the fluorescence quantum yields of metalloles. Based on the suggestion by the prediction model, we synthesized 10 fluorescent molecules to confirm the prediction accuracy. By measuring the fluorescence quantum yields of the synthesized molecules, it was demonstrated that our prediction model reasonably classified the quantum yields with an accuracy of 0.7. In particular, the low quantum yields were perfectly predicted for the synthesized molecules, demonstrating the usefulness of our prediction model to screen out weakly fluorescent molecules from the candidates. On the other hand, the low precision of 0.5 was attributed to the bias in the training dataset containing many fluorine-containing molecules with high quantum yields. Our prediction model was then revised with the generator of candidate molecular structures for more efficient development of fluorescent materials with taking the applicability domain into account, and the improvement of the applicability was confirmed owing to the increment of the dataset.https://www.mdpi.com/1420-3049/30/8/1686fluorescent materialsmachine learningmodel-based research |
| spellingShingle | Yusuke Kanematsu Akiyoshi Ohta Shunya Nagai Yohei Adachi Hiromasa Kaneko Takayoshi Ishimoto Takio Kurita Joji Ohshita A Practical Application of Machine Learning for the Development of Metallole-Based Fluorescent Materials Molecules fluorescent materials machine learning model-based research |
| title | A Practical Application of Machine Learning for the Development of Metallole-Based Fluorescent Materials |
| title_full | A Practical Application of Machine Learning for the Development of Metallole-Based Fluorescent Materials |
| title_fullStr | A Practical Application of Machine Learning for the Development of Metallole-Based Fluorescent Materials |
| title_full_unstemmed | A Practical Application of Machine Learning for the Development of Metallole-Based Fluorescent Materials |
| title_short | A Practical Application of Machine Learning for the Development of Metallole-Based Fluorescent Materials |
| title_sort | practical application of machine learning for the development of metallole based fluorescent materials |
| topic | fluorescent materials machine learning model-based research |
| url | https://www.mdpi.com/1420-3049/30/8/1686 |
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