密度泛函理论
线性回归
计算机科学
简单(哲学)
集合(抽象数据类型)
极限(数学)
回归
数据集
分布(数学)
算法
应用数学
计算化学
化学
数学
人工智能
机器学习
统计
数学分析
哲学
认识论
程序设计语言
作者
Surajit Nandi,Jonas Busk,Peter Bjørn Jørgensen,Tejs Vegge,Arghya Bhowmik
标识
DOI:10.1021/acs.jcim.2c00760
摘要
Workflows to predict chemical reaction networks based on density functional theory (DFT) are prone to systematic errors in reaction energy due to the extensive use of cheap DFT exchange-correlation functionals to limit computational cost. Recently, machine learning-based models are increasingly applied to mitigate this problem. However, machine learning models require systems similar to trained data, and the models often perform poorly for out-of-distribution systems. Here, we present a simple bond-based correction method that improves the accuracy of DFT-derived reaction energies. It is based on linear regression, and the correction terms for each bond are derived from reactions among the QM9 data set. We demonstrate the effectiveness of this method with three DFT functionals in three different rungs of Jacob's ladder. The simple correction method is effective for all rungs but especially so for the cheapest PBE functional. Finally, we applied the correction method to a few reactions with molecules significantly different from those in the QM9 data set that was used to fit the linear regression model. Once corrected by this method, we found that the DFT reaction energies for such out-of-distribution reactions are within 0.05 eV of the G4MP2 method.
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