Δ-ML approach drastically reduces mean absolute error (MAE) in atomization enthalpies for PM7 and B3LYP methods in respect to accurate G4MP2 values
In our recent study, we propose using machine learning (ML) to correct differences in properties calculated at two quantum chemical (QC) methods with different accuracy.
In the Δ-ML approach ML model is trained on differences between some property \(P_t\) calculated at the target level of theory and property \(P’_b\) calculated at the baseline level of theory. Then this ML model is used to predict \(\Delta_b^t\) for out-of-sample molecules.
