Recent advancements in the use of Δ-learning for (bio)molecular simulations have exemplified its great potential to develop a more generalizable hybrid QM/ML computational method. For instance, the AIQM1 model (general-purpose artificial intelligence QM method 1) (21) was developed by targeting the energy differences between the orthogonalization-and dispersion-corrected method 2 Hamiltonian (ODM2) and CCSD(T)*/CBS (66) calculations from the ANI-1ccx dataset (67), yielding "gold-standard" coupled-cluster accuracies for the ground-state properties, such as energies and geometries, of closed-shell, neutral organic compounds like polyynes, molecular dimers, and water clusters. Another notable Δ-learning model is PM6-ML , which integrates the robust PM6-D3H4X semiempirical method with the TorchMD-NET architecture to achieve DFT-level accuracy. PM6-ML covers a broad chemical space, ranging from small peptides to protein-binding complexes, and is capable of predicting interaction energies, conformational energies, torsional profiles, and optimized geometries. In a similar vein, the XPaiNN model ( ) was developed to enhance the performance of GFN2-xTB (70) using the PaiNN architecture . Indeed, XPaiNN has demonstrated improved accuracy and transferability in investigating large conjugated compounds, non-covalent intermolecular interactions, and transition states.