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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2509.21362 |
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| _version_ | 1866909806584922112 |
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| author | Karpov, Kirill V. Pikulin, Ivan S. Bokov, Grigory V. Mitrofanov, Artem A. |
| author_facet | Karpov, Kirill V. Pikulin, Ivan S. Bokov, Grigory V. Mitrofanov, Artem A. |
| contents | The properties of complexes with transuranium elements have long been the object of research in various fields of chemistry. However, their experimental study is complicated by their rarity, high cost and special conditions necessary for working with such elements, and the complexity of quantum chemical calculations does not allow their use for large systems. To overcome these problems, we used modern machine learning methods to create a novel neural network architecture that allows to use available experimental data on a number of elements and thus significantly improve the quality of the resulting models. We also described the applicability domain of the presented model and identified the molecular fragments that most influence the stability of the complexes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21362 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-driven approach to the design of complexing agents for trivalent transuranium elements Karpov, Kirill V. Pikulin, Ivan S. Bokov, Grigory V. Mitrofanov, Artem A. Chemical Physics Machine Learning The properties of complexes with transuranium elements have long been the object of research in various fields of chemistry. However, their experimental study is complicated by their rarity, high cost and special conditions necessary for working with such elements, and the complexity of quantum chemical calculations does not allow their use for large systems. To overcome these problems, we used modern machine learning methods to create a novel neural network architecture that allows to use available experimental data on a number of elements and thus significantly improve the quality of the resulting models. We also described the applicability domain of the presented model and identified the molecular fragments that most influence the stability of the complexes. |
| title | Data-driven approach to the design of complexing agents for trivalent transuranium elements |
| topic | Chemical Physics Machine Learning |
| url | https://arxiv.org/abs/2509.21362 |