Universal QM/MM Approaches for General Nanoscale Applications

TL;DR

This paper reviews automated QM/MM modeling approaches that enable reliable, fast, and standardized construction of hybrid models for nanoscale applications, emphasizing automation, parameterization, and error control.

Contribution

It provides a comprehensive overview of current automated QM/MM methods, focusing on model parametrization, region selection, and embedding schemes for broad nanoscale applications.

Findings

Highlights the importance of automation in QM/MM model construction.

Discusses methods for physically-motivated QM region selection.

Emphasizes the role of uncertainty quantification and error mitigation.

Abstract

Quantum mechanics/molecular mechanics (QM/MM) hybrid models allow one to address chemical phenomena in complex molecular environments. Whereas this modeling approach can cope with a large system size at moderate computational costs, the models are often tedious to construct and require manual preprocessing and expertise. As a result, transferability to new application areas can be limited and the many parameters are not easy to adjust to reference data that are typically scarce. Therefore, it is desirable to devise automated procedures of controllable accuracy, which enables such modeling in a standardized and black-box-type manner. Although diverse best-practice protocols have been set up for the construction of individual components of a QM/MM model (e.g., the MM potential, the type of embedding, the choice of the QM region), automated procedures that reconcile all steps of the QM/MM model construction are still rare. Here, we review the state of the art of QM/MM modeling with a focus on automation. We elaborate on MM model parametrization, on atom-economical physically-motivated QM region selection, and on embedding schemes that incorporate mutual polarization as critical components of the QM/MM model. In view of the broad scope of the field, we mostly restrict the discussion to methodologies that build \textit{de novo} models based on first-principles data, on uncertainty quantification, and on error mitigation with a high potential for automation. Ultimately, it is desirable to be able to set up reliable QM/MM models in a fast and efficient automated way without being constrained by specific chemical or technical limitations.