Optimal spectroscopic measurement design: Bayesian framework for rational data acquisition
We propose an optimal experimental design method for spectroscopic measurements that can determine the appropriate number and placement of measurement points in a rational manner. Spectroscopic measurements are fundamental for material characterization. It is essential to determine the optimal exper...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/add0f6 |
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| Summary: | We propose an optimal experimental design method for spectroscopic measurements that can determine the appropriate number and placement of measurement points in a rational manner. Spectroscopic measurements are fundamental for material characterization. It is essential to determine the optimal experimental conditions in an automated, mathematically guaranteed manner for rational and autonomous experiments; however, these conditions have traditionally been determined on the basis of the intuition of human experts. In this work, we developed a method for extracting prior information from a standard spectral database and incorporating it into the Bayesian experimental design framework to determine the optimal measurement points automatically. We verified the proposed method by applying it to x-ray absorption spectrum measurements and evaluated its optimality through conventional analysis. We found that only 70% of the measurement points used in previous studies were sufficient and that the obtained points are consistent with the experts’ intuition. The proposed method is expected to enable more rational and efficient fully automated experiments in the future. |
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| ISSN: | 2632-2153 |