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Mitigating error cancellation in density functional approximations via machine learning correction

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Novel approach optimizes B3LYP functional in thermochemical and kinetic energy calculations, suggesting improved transferability.

ML-based correction significantly enhances the accuracy of density functional approximations.
Tests reveal that the ML model outperforms the original B3LYP, improving predictions in thermochemical calculations.
This method eliminates reliance on error cancellation between chemical species in density functional theory.
Utilizing absolute energies as reference data, the approach enables the development of robust density functional approximations.

Zipeng et al. (2025) studied this question.

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