Charge redistribution in QM:QM ONIOM model systems: a constrained density functional theory approach

ONIOM Method with Charge Transfer Corrections (ONIOM-CT): Analytic Gradients and Benchmarking

Hybrid methods such as ONIOM that treat different regions of a large molecule using different methods are widely used to investigate chemical reactions in a variety of materials and biological systems. However, there are inherent sources of significant errors due to the standard treatment of the boundary between the regions using hydrogen link atoms. In particular, an unbalanced charge distribution in the chemically important model region is a potential source of such problems. We have previously suggested ONIOM-CT (ONIOM with charge transfer corrections) which addresses this issue by applying a potential in the form of point charges to obtain a desired charge redistribution. The metric for charge redistribution relies on the type of population analysis used to obtain the charges. ONIOM-CT has been implemented using Mulliken and Löwdin population analyses and has been shown to improve computed reaction energies for illustrative chemical reactions. In this work, we derive and implement the analytic gradients for ONIOM-CT that requires solving two sets of coupled-perturbed self-consistent equations, one each for the model system and the full system. However, both are needed only at the low level of theory, allowing for an efficient formulation and implementation for both Mulliken and Löwdin population analyses. Benchmarking and illustrative geometry optimizations have been carried out for a previously studied set of reactions involving a single link atom between regions. Additionally, we have generalized our method for the treatment of model systems involving multiple link atoms to enable applications for a broader set of problems. The generalized methods are illustrated for both charge models. Furthermore, we have studied a set of three proton transfer reactions and demonstrate that significant improvement is achieved by ONIOM-CT over ONIOM using both Mulliken and Löwdin population analyses.