Cheap Turns Superior: A Linear Regression-Based Correction Method to Reaction Energy from the DFT

Surajit Nandi, Jonas Busk, Peter Bjørn Jørgensen, Tejs Vegge, Arghya Bhowmik*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

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.
Original languageEnglish
JournalJournal of Chemical Information and Modeling
Volume62
Issue number19
Pages (from-to)4727-4735
Number of pages9
ISSN1549-9596
DOIs
Publication statusPublished - 2022

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