Khouchen Malak, Klar Paul Benjamin, Chintakindi Hrushikesh, Suresh Ashwin, Palatinus Lukas
Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic.
University of Bremen, Bremen, Germany.
Acta Crystallogr A Found Adv. 2023 Sep 1;79(Pt 5):427-439. doi: 10.1107/S2053273323005053. Epub 2023 Aug 14.
Estimating the error in the merged reflection intensities requires a full understanding of all the possible sources of error arising from the measurements. Most diffraction-spot integration methods focus mainly on errors arising from counting statistics for the estimation of uncertainties associated with the reflection intensities. This treatment may be incomplete and partly inadequate. In an attempt to fully understand and identify all the contributions to these errors, three methods are examined for the correction of estimated errors of reflection intensities in electron diffraction data. For a direct comparison, the three methods are applied to a set of organic and inorganic test cases. It is demonstrated that applying the corrections of a specific model that include terms dependent on the original uncertainty and the largest intensity of the symmetry-related reflections improves the overall structure quality of the given data set and improves the final R factor. This error model is implemented in the data reduction software PETS2.
估计合并反射强度中的误差需要全面了解测量中所有可能的误差来源。大多数衍射斑点积分方法主要关注因计数统计产生的误差,以估计与反射强度相关的不确定性。这种处理可能不完整且部分不充分。为了全面理解和识别这些误差的所有贡献,研究了三种方法来校正电子衍射数据中反射强度的估计误差。为了进行直接比较,将这三种方法应用于一组有机和无机测试案例。结果表明,应用包含取决于原始不确定性和对称相关反射最大强度的项的特定模型进行校正,可提高给定数据集的整体结构质量并改善最终的R因子。此误差模型已在数据处理软件PETS2中实现。