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Space Group Approximation of a Molecular Crystal by Classifying Molecules for Their Electric Potentials and Roughness on Their Inertial Ellipsoid Surface

机译:通过对分子的电势和惯性椭球表面的粗糙度进行分类,对分子晶体进行空间群近似

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In order to predict the most probable space group where a molecule crystallizes, it is assumed that molecular shape and electric potential distribution on the molecular surface are the main factors or predictors. However, to compare and classify molecules by these two factors seems to be very difficult for in general such different objects. Thus, in order to compare molecules, they are reduced to their inertial ellipsoid in which surface 26 equally spaced points were chosen where a roughness factor and an electric potential due to all atomic charges of the whole molecule are calculated. By this procedure, different molecules encoded by these two predictor vectors can be compared and classified, showing that molecules that crystallize in the same space group have more similar predictor vectors. This result opens the possibility to predict the more probable spatial group associated with a molecule.
机译:为了预测分子结晶的最可能的空间群,假定分子形状和分子表面上的电势分布是主要因素或预测因子。然而,对于这两个不同的对象,通过这两个因素对分子进行比较和分类似乎非常困难。因此,为了比较分子,将它们简化为惯性椭球,在惯性椭球中选择了表面等距的26个点,并计算了整个分子的所有原子电荷引起的粗糙度因子和电势。通过此过程,可以比较和分类由这两个预测变量矢量编码的不同分子,这表明在同一空间组中结晶的分子具有更相似的预测变量矢量。该结果为预测与分子相关的更可能的空间基团提供了可能性。

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