使用机器学习预测C-N交叉合中的反应性能
Derek T Ahneman1, Jesús G Estrada1, Shishi Lin2
1Department of Chemistry, Princeton University, Princeton, NJ 08544, USA.
概括
机器学习可以预测合成反应的性能. 随机森林模型准确地预测复杂化学反应的产量,在线性回归上改进以更广泛地采用合成方法.
科学领域:
- 化学学
- 计算化学
- 化学工程
背景情况:
- 机器学习在科学研究中越来越重要.
- 预测化学反应的结果对于优化合成过程至关重要.
- 高通量实验 (HTE) 产生了用于ML模型训练的大数据集.
研究的目的:
- 调查ML对预测合成反应性能的有用性.
- 将ML算法与化学领域的传统方法进行比较.
- 评估ML模型的适用性,以促进合成方法的采用.
主要方法:
- 提取了反应元件的原子,分子和振动描述符.
- 使用催化布赫瓦尔德-哈特维格交叉合反应作为模型系统.
- 使用随机森林和线性回归算法用于使用HTE数据的预测建模.
主要成果:
- 随机森林算法在预测反应产量方面显著优于线性回归.
- 即使在训练数据集稀疏的情况下,ML模型也表现出强大的性能.
- 获得了成功的样本外预测,验证了模型的通用性.
结论:
- 机器学习,特别是随机森林,可以准确预测合成反应的性能.
- 这种方法对于导航多维化学空间和优化反应具有价值.
- 基于机器学习的预测可以加速新合成方法的采用和开发.
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