使用DFT计算和图形注意力网络模型预测周期性高价值素试剂的键解离能
Yingbo Shao1, Zhiyuan Ren1, Zhihui Han1
1State Key Laboratory of Elemento-Organic Chemistry, College of Chemistry, Nankai University, Tianjin 300071, P. R. China.
本研究探讨了高价值 (III) 和 (III) 化合物,使用键解离能 (BDE) 分析它们的稳定性. 机器学习模型预测BDE,有助于开发这些研究较少的超价原体试剂.
科学领域:
- 有机化学 有机化学
- 计算化学的计算化学
背景情况:
- 超价 (III) 试剂在有机合成中被广泛使用.
- 由于合成和稳定性挑战,高价值 (III) 和 (III) 试剂的探索较少.
研究的目的:
- 为了研究循环超价化合物的同质和异质键解离能 (BDE).
- 评估这些化合物的化学稳定性和功能组转移能力.
- 使用机器学习开发BDE的预测模型.
主要方法:
- 用密度函数理论 (DFT) 分析来计算BDE.
- 机器学习算法被用来构建BDE的预测模型.
主要成果:
- 在不同素中心的同质BDE之间发现了中等的线性相关性.
- 在各种素中心的异质BDE中观察到强烈的线性相关性.
- 开发了同质和异质BDE的预测模型.
结论:
- 这些发现提供了关于高价值 (III) 和 (III) 化合物的稳定性和反应性的见解.
- 预测模型可以促进化学稳定性和功能组转移的估计.
- 这项研究支持未来开发新的高价值素试剂.
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