机器学习和密度函数理论计算的协同作用,用于预测实验性易斯基亲和力和易斯多基结合原子
Hieu Huynh1, Khanh Le1, Linh Vu1
1Fulbright University Vietnam, Ho Chi Minh City, Vietnam.
Journal of computational chemistry
|March 19, 2024
概括
机器学习模型准确地预测易斯酸相互作用,特别是三化物 (BF3) 亲和力,使用密度函数理论计算. 这种方法有效地估计了那些很难通过实验测量的特性.
科学领域:
- 计算化学是一种计算化学.
- 量子化学是一种量子化学.
- 化学信息学 化学信息学
背景情况:
- 易斯酸相互作用是化学的基础.
- 量化预测这些相互作用可能在实验上具有挑战性或在计算上密集.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测实验性易斯酸相互作用,特别是三化物 (BF3) 亲和力.
- 评估ML模型在预测易斯基原子在多基中的相对结合强度方面的能力.
主要方法:
- 使用ab initio计算,特别是密度函数理论 (DFT),生成一个数据集超过3000个BF3 adducts.
- 训练ML模型使用DFT计算的BF3亲和关系和RDKit产生的输入特征.
- 使用皮尔森相关系数和平均绝对误差验证ML模型性能.
主要成果:
- 机器学习模型在预测实验BF3亲和度方面取得了很高的准确性,Pearson相关系数约为0.9,平均绝对误差约为10kJ/mol.
- 开发的ML模型证明了预测易斯基原子在多基中的相对结合强度的能力.
结论:
- 结合DFT计算和ML模型,为预测实验化学性质提供了一个强大而高效的策略.
- 这种方法对于通过传统的实验或计算方法获得稀缺或不切实际的属性特别有价值.
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