论文标题
通过机器学习校正半经验计算的屏障高度预测
Barrier height prediction by machine learning correction of semiempirical calculations
论文作者
论文摘要
目前的工作中提出了不同的机器学习（ML）模型，以预测半经验量子力学（SQM）计算的DFT质量屏障高度（BHS）。 ML模型包括多任务深神经网络，通过XGBoost界面通过梯度增强树和高斯过程回归。考虑到相同数量的数据点，获得的平均绝对误差（MAE）比以前的模型相似或稍好。与其他用于预测BHS的ML模型不同，包括熵效应，这可以在不同温度下预测速率常数。本文提出的ML校正可能有助于快速筛选燃烧化学或星体化学中出现的大反应网络。最后，我们的结果表明，有70％的定制预测因子是对模型输出影响最大的功能之一。这套定制的预测因子可以由未来的Delta-ML模型使用，以改善其他反应特性的定量预测。
Different machine learning (ML) models are proposed in the present work to predict DFT-quality barrier heights (BHs) from semiempirical quantum-mechanical (SQM) calculations. The ML models include multi-task deep neural network, gradient boosted trees by means of the XGBoost interface, and Gaussian process regression. The obtained mean absolute errors (MAEs) are similar or slightly better than previous models considering the same number of data points. Unlike other ML models employed to predict BHs, entropic effects are included, which enables the prediction of rate constants at different temperatures. The ML corrections proposed in this paper could be useful for rapid screening of the large reaction networks that appear in Combustion Chemistry or in Astrochemistry. Finally, our results show that 70% of the bespoke predictors are amongst the features with the highest impact on model output. This custom-made set of predictors could be employed by future delta-ML models to improve the quantitative prediction of other reaction properties.
