five

Crystal Structure Prediction of Aminols: Advantages of a Supramolecular Synthon Approach with Experimental Structures

收藏
Figshare2016-05-05 更新2026-05-11 收录
下载链接:
https://figshare.com/articles/dataset/Crystal_Structure_Prediction_of_Aminols_Advantages_of_a_Supramolecular_Synthon_Approach_with_Experimental_Structures/3274720
下载链接
链接失效反馈
官方服务:
资源简介:
The supramolecular synthon approach to crystal structure prediction (CSP) takes into account the complexities inherent in crystallization. The synthon is a kinetically favored unit, and through analysis of commonly occurring synthons in a group of related compounds, kinetic factors are implicitly invoked. The working assumption is that while the experimental structure need not be at the global minimum, it will appear somewhere in a list of computationally generated structures so that it can be suitably identified and ranked upward using synthon information. These ideas are illustrated with a set of aminophenols, or aminols. In the first stage, a training database is created of the 10 isomeric methylaminophenols. The crystal structures of these compounds were determined. The prototypes 2-, 3-, and 4-aminophenols were also included in the training database. Small and large synthons in these 13 crystal structures were then identified. Small synthons are of high topological but low geometrical value and are used in negative screens to eliminate computationally derived structures that are chemically unreasonable. Large synthons are more restrictive geometrically and are used in positive screens ranking upward predicted structures that contain these more well-defined patterns. In the second stage, these screens are applied to CSP of nine new aminols carried out in 14 space groups. In each space group, up to 10 lowest energy structures were analyzed with respect to their synthon content. The results are encouraging, and the predictions were classified as good, unclear, or bad. Two predictions were verified with actual crystal structure determinations.
创建时间:
2016-05-05
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作