Innovative Sample Structure Prediction using Bayesian Estimation and XPS Simulator

抄録

We have developed a framework that integrates the XPS simulator SESSA into Bayesian estimation to solve the inverse problem of XPS. This approach automates the typically labor-intensive task of manually adjusting sample structure parameters for XPS simulation. By incorporating Bayesian methods, we can estimate the distribution of plausible sample structures based on experimental XPS data, providing not only an optimal solution but also a detailed visualization of the solution's distribution. In a case study, we performed angle-resolved XPS on a four-layered sample and successfully estimated the sample structure using this framework. This method streamlines the analysis of XPS data and offers a comprehensive view of sample structures, marking a significant step forward in the application of data science techniques to experimental data.