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Data-driven sensitivity analysis in surface structure determination using total-reflection high-energy positron diffraction (TRHEPD)
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DOI：10.1016/j.cpc.2021.108186.png)
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Abstract
En 中文
The present article proposes a data analysis method for experimentally-derived measurements, which consists of an auto-optimization procedure and a sensitivity analysis. The method was applied to the results of a total-reflection high-energy positron diffraction (TRHEPD) experiment, a novel technique of determining surface structures or the position of the atoms near the material surface. This method solves numerically the partial differential equation in the fully-dynamical quantum diffraction theory with many trial surface structures. In the sensitivity analysis, we focused on the experimental uncertainties and the variation over individual fitting parameters, which was analyzed by solving the eigenvalue problem of the variance-covariance matrix. A modern massively parallel supercomputer was used to complete the analysis within a moderate computational time. The sensitivity analysis provides a basis for the choice of variables in the data analysis for practical reliability. The effectiveness of the present analysis method was demonstrated in the structure determination of a Si4O5N3/6H-SiC(0001)-(root 3x root 3) R30 degrees surface. Furthermore, this analysis method is applicable to many experiments other than TRHEPD. (C) 2021 The Author(s). Published by Elsevier B.V.
Keywords:
Data analysis method for measurement experiments
Material surface structure
Total-reflection high-energy positron
diffraction experiment
Variance-covariance matrix
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