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Why direct and post-refinement determinations of absolute structure may give different results

机译:为什么直接和精炼后的绝对结构测定可能会得出不同的结果

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摘要

Direct determination of the Flack parameter as part of the structure refinement procedure usually gives different, though similar, values to post-refinement methods. The source of this discrepancy has been probed by analysing a range of data sets taken from the recent literature. Most significantly, it was observed that the directly refined Flack (x) parameter and its standard uncertainty are usually not much influenced by changes in the refinement weighting schemes, but if they are then there are probably problems with the data or model. Post-refinement analyses give Flack parameters strongly influenced by the choice of weights. Weights derived from those used in the main least squares lead to post-refinement estimates of the Flack parameters and their standard uncertainties very similar to those obtained by direct refinement. Weights derived from the variances of the observed structure amplitudes are more appropriate and often yield post-refinement Flack parameters similar to those from direct refinement, but always with lower standard uncertainties. Substantial disagreement between direct and post-refinement determinations are strongly indicative of problems with the data, which may be difficult to identify. Examples drawn from 28 structure determinations are provided showing a range of different underlying problems. It seems likely that post-refinement methods taking into account the slope of the normal probability plot are currently the most robust estimators of absolute structure and should be reported along with the directly refined values.
机译:作为结构优化过程的一部分,直接确定Flack参数通常会提供与后优化方法不同(尽管相似)的值。通过分析最近文献中的一系列数据来探究这种差异的根源。最重要的是,观察到直接精炼的Flack(x)参数及其标准不确定性通常不受精炼权重方案更改的影响很大,但是如果存在,则数据或模型可能存在问题。细化后的分析使Flack参数受到权重选择的强烈影响。从主要最小二乘法中使用的权重得出的权重导致对Flack参数进行精化后估计,其标准不确定性与通过直接精制获得的极度相似。从观察到的结构振幅的方差得出的权重更合适,并且通常会产生与直接精炼相似的精炼后Flack参数,但始终具有较低的标准不确定性。直接和精炼后确定之间的重大分歧强烈表明数据存在问题,可能难以识别。提供了从28个结构确定中得出的示例,这些示例显示了一系列不同的潜在问题。考虑到正态概率图斜率的后优化方法似乎是当前绝对结构最可靠的估计器,应与直接优化的值一起报告。

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    Watkin, D; Cooper, R;

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  • 年度 2016
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