混合 DFT 几何学和 17k 兰他体复合物的属性─LnQM 数据集
Christian Hölzer1, Igor Gordiy2, Stefan Grimme1
1Mulliken Center for Theoretical Chemistry, University of Bonn, 53115 Bonn, Germany.
这项研究介绍了17,269个类复合物的综合数据集,使量子化学和机器学习进行比较分析. 这些数据有助于对类物质的特性进行独立研究,这对推进材料科学至关重要.
科学领域:
- 无机化学 无机化学 有机化学
- 计算化学计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 兰他诺酸具有独特的特性,推动着研究和工业.
- 现有的分子数据集缺乏对类分子的全面比较数据.
- 对于支持量子化学和机器学习的数据集存在差距.
研究的目的:
- 创建一个全面的,精心策划的数据集的单类复合体.
- 为了促进对兰他诺因特性的比较研究,独立于连结体效应.
- 为了支持量子化学计算和数据科学应用的进步.
主要方法:
- 在+3氧化状态下,在所有15种类中生成了17269个单类复合体.
- 在联结体结构中系统地变换了中央类原子,以便进行比较.
- 包括各种计算功能:优化几何形状,能量,电子特性和电荷分析.
主要成果:
- 该数据集涵盖了所有15种兰他诺体,各种分子电荷和高自旋倍数.
- 提供了PBE0-D4/def2-SVP优化的几何形状和 ωB97M-V/def2-SVPD能量.
- 包括广泛的电子结构数据 (HOMO-LUMO,人口分析) 和物理特性 (双极时刻,极化性).
结论:
- 提出的数据集弥合了对比兰他类药物研究的差距.
- 它可以独立研究兰他诺酸对复杂性质的影响.
- 这些公开可访问的数据作为未来兰他诺इड研究的基础.
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