Quantitative analysis of EXAFS data sets using deep reinforcement learning
Abstract Extended X-ray absorption fine structure (EXAFS) serves as a unique tool for accurately characterizing the local structural properties surrounding specific atoms. However, the quantitative analysis of EXAFS data demands significant effort. Artificial intelligence (AI) techniques, including...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-94376-5 |
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| author | Eun-Suk Jeong In-Hui Hwang Sang-Wook Han |
| author_facet | Eun-Suk Jeong In-Hui Hwang Sang-Wook Han |
| author_sort | Eun-Suk Jeong |
| collection | DOAJ |
| description | Abstract Extended X-ray absorption fine structure (EXAFS) serves as a unique tool for accurately characterizing the local structural properties surrounding specific atoms. However, the quantitative analysis of EXAFS data demands significant effort. Artificial intelligence (AI) techniques, including deep reinforcement learning (RL) methods, present a promising avenue for the rapid and precise analysis of EXAFS data sets. Unlike other AI approaches, a deep RL method utilizing reward values does not necessitate a large volume of pre-prepared data sets for training the neural networks of the AI system. We explored the application of a deep RL method for the quantitative analysis of EXAFS data sets, utilizing the reciprocal of the R-factor of a fit as the reward metric. The deep RL method effectively determined the local structural properties of PtOx and Zn-O complexes by fitting a series of EXAFS data sets to theoretical EXAFS calculations without imposing specific constraints. Looking ahead, AI has the potential to independently analyze any EXAFS data, although there are still challenges to overcome. |
| format | Article |
| id | doaj-art-9ce4170bf46740abbb5a22ed832e61d3 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9ce4170bf46740abbb5a22ed832e61d32025-08-20T03:08:25ZengNature PortfolioScientific Reports2045-23222025-05-0115111010.1038/s41598-025-94376-5Quantitative analysis of EXAFS data sets using deep reinforcement learningEun-Suk Jeong0In-Hui Hwang1Sang-Wook Han2Department of Physics Education, Institute of Fusion Science, and Institute of Science Education, Jeonbuk National UniversityPohang Accelerator Laboratory, POSTECHDepartment of Physics Education, Institute of Fusion Science, and Institute of Science Education, Jeonbuk National UniversityAbstract Extended X-ray absorption fine structure (EXAFS) serves as a unique tool for accurately characterizing the local structural properties surrounding specific atoms. However, the quantitative analysis of EXAFS data demands significant effort. Artificial intelligence (AI) techniques, including deep reinforcement learning (RL) methods, present a promising avenue for the rapid and precise analysis of EXAFS data sets. Unlike other AI approaches, a deep RL method utilizing reward values does not necessitate a large volume of pre-prepared data sets for training the neural networks of the AI system. We explored the application of a deep RL method for the quantitative analysis of EXAFS data sets, utilizing the reciprocal of the R-factor of a fit as the reward metric. The deep RL method effectively determined the local structural properties of PtOx and Zn-O complexes by fitting a series of EXAFS data sets to theoretical EXAFS calculations without imposing specific constraints. Looking ahead, AI has the potential to independently analyze any EXAFS data, although there are still challenges to overcome.https://doi.org/10.1038/s41598-025-94376-5Artificial intelligenceReinforcement learningExtended X-ray absorption fine structureMachine learningLocal structural property |
| spellingShingle | Eun-Suk Jeong In-Hui Hwang Sang-Wook Han Quantitative analysis of EXAFS data sets using deep reinforcement learning Scientific Reports Artificial intelligence Reinforcement learning Extended X-ray absorption fine structure Machine learning Local structural property |
| title | Quantitative analysis of EXAFS data sets using deep reinforcement learning |
| title_full | Quantitative analysis of EXAFS data sets using deep reinforcement learning |
| title_fullStr | Quantitative analysis of EXAFS data sets using deep reinforcement learning |
| title_full_unstemmed | Quantitative analysis of EXAFS data sets using deep reinforcement learning |
| title_short | Quantitative analysis of EXAFS data sets using deep reinforcement learning |
| title_sort | quantitative analysis of exafs data sets using deep reinforcement learning |
| topic | Artificial intelligence Reinforcement learning Extended X-ray absorption fine structure Machine learning Local structural property |
| url | https://doi.org/10.1038/s41598-025-94376-5 |
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