特集「データ駆動科学による表面・真空科学研究の新展開」
機械学習によるX線スペクトル計測の効率化
キーワード:
adaptive design of experiment,
Gaussian process regression,
machine learning,
x-ray spectroscopy
2019 年 62 巻 3 号 p. 147-152
詳細
2019 年 62 巻 3 号 p. 147-152
We present an adaptive design of experiment (DoE) by machine learning for X-ray spectroscopy to improve its efficiency. One of the machine learning techniques, Gaussian process regression predicts a spectrum from the experimental data and determines the optimal energy points to measure. Adaptive DoE successfully reduces total energy points to measure as compared to an X-ray magnetic circular dichroism spectroscopy experiment by a conventional DoE. This method has potential applicability to various measurements and reduces the time and cost of experiments.
