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| Main Authors: | , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2503.19425 |
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| _version_ | 1866915415655972864 |
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| author | Yin, Yue Xiao, Hai |
| author_facet | Yin, Yue Xiao, Hai |
| contents | The oxidation state (OS) is an essential chemical concept that embodies chemical intuition but cannot be computed with well-defined physical laws. We establish a data-driven paradigm, with its implementation as Tsinghua Oxidation States in Solids (TOSS), to explicitly compute the OSs in crystal structures as the emergent properties from large-sized datasets based on Bayesian maximum a posteriori probability (MAP). TOSS employs two looping structures over the large-sized dataset of crystal structures to obtain an emergent library of distance distributions as the foundation for chemically intuitive understanding and then determine the OSs by minimizing a loss function for each structure based on MAP and distance distributions in the whole dataset. The application of TOSS to a dataset of $\mathrm{>}$1,000,000 crystal structures delivers a superior success rate, and using the resulting OSs as the dataset, we further train a data-driven alternative to TOSS based on graph convolutional networks. We expect TOSS and the ML-model-based alternative to find a wide spectrum of applications, and this work also demonstrates an encouraging example for the data-driven paradigms to explicitly compute the chemical intuition for tackling complex problems in chemistry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19425 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Oxidation States in Solids from Data-Driven Paradigms Yin, Yue Xiao, Hai Chemical Physics The oxidation state (OS) is an essential chemical concept that embodies chemical intuition but cannot be computed with well-defined physical laws. We establish a data-driven paradigm, with its implementation as Tsinghua Oxidation States in Solids (TOSS), to explicitly compute the OSs in crystal structures as the emergent properties from large-sized datasets based on Bayesian maximum a posteriori probability (MAP). TOSS employs two looping structures over the large-sized dataset of crystal structures to obtain an emergent library of distance distributions as the foundation for chemically intuitive understanding and then determine the OSs by minimizing a loss function for each structure based on MAP and distance distributions in the whole dataset. The application of TOSS to a dataset of $\mathrm{>}$1,000,000 crystal structures delivers a superior success rate, and using the resulting OSs as the dataset, we further train a data-driven alternative to TOSS based on graph convolutional networks. We expect TOSS and the ML-model-based alternative to find a wide spectrum of applications, and this work also demonstrates an encouraging example for the data-driven paradigms to explicitly compute the chemical intuition for tackling complex problems in chemistry. |
| title | Oxidation States in Solids from Data-Driven Paradigms |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2503.19425 |