Optimal Spectroscopic Measurement Design: Bayesian Framework for Rational Data Acquisition

We have proposed an optimal experimental design method for spectroscopic measurement that can determine the appropriate number and placement of measurement points in a rational manner. Spectroscopic measurement is a fundamental experiment for material characterization. It is essential to determine the optimal experimental points automatically for autonomous experiments, however they have traditionally been decided by human expert. In this work, we have developed a method for extracting prior information from a standard spectra 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 by typical analysis. We found that only 70% of the measurement points used in previous studies were sufficient and also the determined points are consistent to the experts' intuition. The proposed method is expected to facilitate more efficient and fully automated experiments in the future.