five

Table 4. Calculated transition state properties for R=iPr (scheme 2)

收藏
Figshare2013-12-05 更新2026-04-29 收录
下载链接:
https://figshare.com/articles/dataset/Table_4_Calculated_transition_state_properties_for_R_iPr_scheme_2_/840484
下载链接
链接失效反馈
官方服务:
资源简介:
The ten year old Houk-List model, for rationalising the origin of stereoselectivity in the organocatalysed intermolecular aldol addition, is revisited using a variety of computational techniques which have been introduced or improved since the original study. Even for such a relatively small system, the role of dispersion interactions is shown to be crucial, along with the use of basis sets where the superposition errors are low. Understanding the non covalent interactions (NCI) at play is highlighted as essential for the design of new synthetic routes and alternative reactants. An NCI analysis of the transition states enables the identification of non-covalent interactions that determine the reaction outcome, confirming the role of the electrostatic NCHᵟ+∙∙∙Oᵟ- interactions and highlighting new geometric schemes based on dispersion. Alternative mechanisms, such as proton-relays involving a water molecule or the Hajos-Parrish alternative, are shown to be higher in energy. The Amsterdam manifesto, which espouses the principle that scientific data should be citable, is followed here by using interactive data tables assembled via calls to the data DOI (digital-object-identifiers) held on a digital repository.

已有十年历史的霍克-利斯特模型(Houk-List model),用于阐释有机催化分子间羟醛加成反应中立体选择性的起源,本次研究借助原始研究发表后涌现或优化的各类计算技术对其进行了重新审视。即便针对此类规模相对较小的反应体系,色散相互作用与低重叠误差基组的应用均被证实起到了关键作用。研究着重指出,明晰反应中参与作用的非共价相互作用(non covalent interactions, NCI),是设计新型合成路线与替代反应物的必要前提。对过渡态开展的非共价相互作用分析,能够精准识别决定反应结果的非共价相互作用,验证了静电性NCHδ+∙∙∙Oδ-相互作用的核心地位,并揭示了基于色散作用的新型几何作用模式。诸如涉及水分子的质子接力机制或霍乔斯-帕里希替代路径在内的其他反应机制,均被证实具有更高的能量壁垒。本研究遵循《阿姆斯特丹宣言》(Amsterdam manifesto)中“科学数据应可被引用”的原则,通过调用数字仓储中存储的数字对象标识符(digital-object-identifiers, DOI)关联的交互式数据表,实现了研究数据的可引用性。
创建时间:
2013-12-05
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