Transferable and Transparent Energy Decomposition-Based Machine Learning Models for Computing Accurate Reaction Energetics.

ABSTRACT: We present a transferable, interpretable, and modular machine-learning framework that enhances the accuracy of density functional theory (DFT) reaction energies using physically meaningful energy-decomposition descriptors. Reaction energies computed at the DFT level with standard basis sets are first decomposed into chemically intuitive contributions─such as kinetic and potential energy─which are then used to train a library of linear regression (LR) models. This includes a general-purpose model that reduces mean absolute percentage errors (MAPE) relative to gold standard CCSD(T)/CBS reference values by up to 63% compared to uncorrected DFT across extended benchmark sets. In parallel, a series of specialized LR models provide improved accuracy for specific reaction classes. A random forest

SUBMITTER: Jacinto-Mejia CR 

PROVIDER: S-EPMC12613314 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature