论文标题
通过机器学习对分子和过渡状态分区功能的低成本预测
Low-cost prediction of molecular and transition state partition functions via machine learning
论文作者
论文摘要
我们已经生成了一个超过30000个有机化学气相分区功能的开源数据集。通过这些数据，训练了一项机器学习深神经网络估计器，以预测未知有机化学气体相变状态的分配功能。该估计量仅依赖于反应物和产品几何形状和分区功能。第二台机器学习深神经网络经过训练，以预测化学物种从其几何形状中的分配功能。我们的模型准确地预测了测试集分区功能的对数，最大平均绝对误差为2.7％。因此，这种方法提供了一种降低计算反应速率常数的成本的手段。这些模型还用于计算过渡状态理论反应速率常数预成分，结果与相应的从头算机计算的定量一致，其准确度在日志尺度上为98.3％。
We have generated an open-source dataset of over 30000 organic chemistry gas phase partition functions. With this data, a machine learning deep neural network estimator was trained to predict partition functions of unknown organic chemistry gas phase transition states. This estimator only relies on reactant and product geometries and partition functions. A second machine learning deep neural network was trained to predict partition functions of chemical species from their geometry. Our models accurately predict the logarithm of test set partition functions with a maximum mean absolute error of 2.7%. Thus, this approach provides a means to reduce the cost of computing reaction rate constants ab initio. The models were also used to compute transition state theory reaction rate constants prefactors and the results were in quantitative agreement with the corresponding ab initio calculations with an accuracy of 98.3% on the log scale.
