机器学习从量子化学来预测实验溶剂对反应速率的影响
Yunsie Chung1, William H Green1
1Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge MA 02139 USA whgreen@mit.edu.
Chemical science
|February 16, 2024
概括
这项研究开发了一种机器学习模型,用于预测溶剂对反应速率的影响,这对于化学过程至关重要. 该模型准确地预测了各种反应和溶剂的溶解能和速率常数.
科学领域:
- 计算化学是一种计算化学.
- 化学动力学 化学动力学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 准确预测溶剂对反应速率的影响对于化学过程设计和高通量选至关重要.
- 现有的机器学习模型受到数据稀缺的限制,阻碍了在各种反应和溶剂中进行概括.
研究的目的:
- 开发一种可通用的机器学习模型,用于预测动态溶剂效应.
- 为了预测溶解自由能量和溶解度激活的溶解相反应.
主要方法:
- 使用COSMO-RS方法生成了超过28000个中性反应和295种溶剂的大数据集.
- 训练了一种机器学习模型来预测溶解自由能量 (ΔΔG‡solv) 和激活的溶解度 (ΔΔH‡solv).
- 在未见的反应和实验数据上验证了模型.
主要成果:
- 与COSMO-RS计算相比,达成的平均绝对误差为ΔΔG‡solv的0.71 kcal mol−1和ΔΔH‡solv的1.03 kcal mol−1.
- 在实验数据上提供了对相对速率常数在4的系数内可靠的预测.
- 该模型提供基于原子映射反应和溶剂SMILES字符串的近瞬间预测.
结论:
- 开发的机器学习模型有效地预测了各种反应和溶剂的动态溶剂效应.
- 这种工具可以通过提供快速而准确的预测来加速化学过程设计和溶剂选.
- 该模型克服了数据限制,使其在计算化学和反应动力学中具有更广泛的适用性.
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