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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.08539 |
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| _version_ | 1866909779903905792 |
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| author | Harashima, Yosuke Miyake, Takashi Baba, Ryuto Takayama, Tomoaki Takasuka, Shogo Shigeta, Yasuteru Yamaguchi, Yuichi Kudo, Akihiko Fujii, Mikiya |
| author_facet | Harashima, Yosuke Miyake, Takashi Baba, Ryuto Takayama, Tomoaki Takasuka, Shogo Shigeta, Yasuteru Yamaguchi, Yuichi Kudo, Akihiko Fujii, Mikiya |
| contents | This study proposes a materials search method combining a data assimilation technique based on a multivariate Gaussian distribution with Bayesian optimization. The efficiency of the search using this method was demonstrated using a pair of example functions. By combining Bayesian optimization with the data assimilation technique, the maximum value of the example function was found more efficiently compared to ordinary Bayesian optimization without the data assimilation. A practical demonstration was also conducted by constructing a data assimilation model for the bandgap of (Sr$_{1-x_{1}-x_{2}}$La$_{x_{1}}$Na$_{x_{2}}$)(Ti$_{1-x_{1}-x_{2}}$Ga$_{x_{1}}$Ta$_{x_{2}}$)O$_{3}$. The concentration dependence of the bandgap was analyzed, and synthesis was performed with chemical compositions in the sparse region of the training data points to validate the predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08539 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Covariance Linkage Assimilation method for Unobserved Data Exploration Harashima, Yosuke Miyake, Takashi Baba, Ryuto Takayama, Tomoaki Takasuka, Shogo Shigeta, Yasuteru Yamaguchi, Yuichi Kudo, Akihiko Fujii, Mikiya Materials Science This study proposes a materials search method combining a data assimilation technique based on a multivariate Gaussian distribution with Bayesian optimization. The efficiency of the search using this method was demonstrated using a pair of example functions. By combining Bayesian optimization with the data assimilation technique, the maximum value of the example function was found more efficiently compared to ordinary Bayesian optimization without the data assimilation. A practical demonstration was also conducted by constructing a data assimilation model for the bandgap of (Sr$_{1-x_{1}-x_{2}}$La$_{x_{1}}$Na$_{x_{2}}$)(Ti$_{1-x_{1}-x_{2}}$Ga$_{x_{1}}$Ta$_{x_{2}}$)O$_{3}$. The concentration dependence of the bandgap was analyzed, and synthesis was performed with chemical compositions in the sparse region of the training data points to validate the predictions. |
| title | Covariance Linkage Assimilation method for Unobserved Data Exploration |
| topic | Materials Science |
| url | https://arxiv.org/abs/2408.08539 |