Predictive Models for HOMO and LUMO Energies of N‐Donor Heterocycles as Ligands for Lanthanides Separation

Abstract

Quantum chemical calculations combined with QSPR methodology reveal challenging perspectives for the solution of a number of fundamental and applied problems. In this work, we performed the PM7 and DFT calculations and QSPR modeling of HOMO and LUMO energies for polydentate N-heterocyclic ligands promising for the extraction separation of lanthanides because these values are related to the ligands selectivity in the respect to the target cations. Data for QSPR modeling comprised the PM7 calculated HOMO and LUMO energies of N-donor heterocycles, including several types of both known and virtual undescribed polydentate ligands. Ensemble modeling included various molecular fragments as descriptors and different variable selection techniques to build consensus models (CMs) on a training set of 388 ligands using external cross-validation. CMs were then verified to make predictions for two external test sets: 45 ligands (T1) that were similar to the ligands of the training set, and 1546 structures (T2), which were substantially different from the ligands of the training set. The consensus models predict well in 5-fold cross-validation (RMSE =0.097 eV, RMSE =0.064 eV), and on the external test sets (T1: RMSE =0.26 eV, RMSE =0.24 eV; T2: RMSE =0.26 eV, RMSE =0.17 eV). An analysis of the results reveals that substituents in heteroaromatic rings of the ligands and at the amide nitrogens can deeply influence their metal binding properties.

Mapping the Frontier Orbital Energies of Imidazolium-based Cations Using Machine Learning

“…the MAE/RMSE for the external test reaches a maximum of 0.4 eV for HOMO energies and 0.2 eV for LUMO energies, as seen from Figure 3. The training/test data contains cations of the same alkyl chain length and position, 37 Karpov et al reported similar deviations as this study for external test casewith RMSE between 0.17 -0.26 eV for HOMO/LUMO 38. …”

Machine Learning Models for Predicting Zirconocene Properties and Barriers

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