Δ -Machine Learning for the Prediction of Metal Complex Properties

The discovery and design of novel transition metal complexes for specific applications heavily rely on computational high-throughput screenings to identify promising candidates for experimental validation. However, traditional computational approaches, such as density functional theory, are often too computationally demanding to be applied on a large scale. Machine learning methods offer a promising alternative due to their excellent computational efficiency, but their accuracy and high data requirements remain major challenges for their effective implementation. To address these issues, we herein present an adaptation of the Δ-ML strategy for quantum property prediction of transition metal complexes. We combine GFN2-xTB geometry optimizations and density functional theory single-point calculations in order to obtain low-fidelity approximations and generate featurized graph representations that serve as input to a graph neural network architecture. The high-fidelity targets originate from the tmQMg dataset and include the electronic and dispersion energies, HOMO-LUMO gap and dipole moment at the PBE0-D3BJ/def2-TZVP level as well as the polarizability at the PBE-D3BJ/def2-SVP level. Compared to a conventional benchmark approach, the proposed method consistently achieves higher accuracy in the prediction of high-fidelity targets, while demonstrating improved data efficiency and out-of-domain transferability. We furthermore show, how the use of cheaper low-fidelity methods leads to significant reductions in computational cost at minor losses in predictive performance. Overall, these results highlight the potential of Δ-ML for materials discovery in transition metal chemistry, which requires high predictive accuracy despite often times limited availability of training data.