Alkaline earth metal binding with N-heterocyclic carbenes: machine learning-assisted screening of metal-ligand...

N-heterocyclic carbenes (NHCs) are very versatile ligands known for their strong σ-donating ability and stability, making them suitable for forming complexes with alkaline earth metals (AEMs). In this study, we designed a machine learning (ML) based approach for high-throughput screening of NHC-AEM complexes based on their binding energies (BEs), obtained from density functional theory (DFT) optimized structures and single-point domain-based local pair-natural orbital based singles and doubles coupled cluster (DLPNO-CCSD(T)) calculations. The ML models were trained on descriptors derived from fundamental metal properties, RDKit and Sterimol parameters, which were refined through recursive feature refinement steps. The ML workflow involved successive feature refinement steps, beginning with principle component analysis (PCA) filtering (r > 0.9), feature engineering (only in the case of Sterimol parameters), sure independence screening (SIS) based on distance correlation, and stepwise feature elimination. Seven different ML models were trained on the different datasets mentioned above, with the best one achieving a mean square error (MSE) of 0.10 eV². Furthermore, Shapley additive explanations (SHAP) analysis was employed to identify the most influential descriptor governing BE.