应用主动学习来构建可通用的Ni-光电和基合的模型
Lucas W Souza1, Nathan D Ricke1, Braden C Chaffin2
1Global Discovery Chemistry, Novartis, Cambridge, Massachusetts 02139, United States.
这项研究引入了机器学习模型的积极学习方法来预测反应产量,显著改善了化学空间探索. 该方法有效地用最小的数据绘制广的基板空间,优于随机数据选择.
科学领域:
- 计算化学
- 化学中的机器学习
- 有机合成
背景情况:
- 开发用于产量预测的机器学习模型在探索广的条件和基板空间方面面临挑战.
- 高通量实验 (HTE) 对于生成数据至关重要,但需要有效的战略来导航化学多样性.
研究的目的:
- 开发和验证一个积极的学习策略,用于绘制Ni/光电还原催化交叉电友合的基板空间.
- 构建一个大虚拟化学空间的数据效率预测模型.
主要方法:
- 使用主动学习,特别是不确定性查询,快速构建收益预测模型.
- 使用密度函数理论 (DFT) 和差异摩根指纹构建一个随机森林模型.
- 用最小的额外数据将初始模型扩展到新的化学空间.
主要成果:
- 用不到400个数据点构建了22240个化合物的模型.
- 该模型成功扩展到33312个化合物,
- 积极学习模型在预测成功反应方面明显优于基于随机选择数据的模型.
结论:
- 积极学习与DFT特征化相结合,可实现大范围反应的数据效率预测建模.
- 关键的DFT特征,如基LUMO能量,对于模型性能和通用性至关重要.
- 预计这种方法将有助于合成有机化学界有效地构建预测模型.
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