Automated construction of quantum-classical hybrid models

TL;DR

This paper introduces an automated protocol for constructing quantum-classical hybrid models that adaptively determine the QM region based on first principles, enabling high-fidelity simulations with minimal manual intervention.

Contribution

It extends the SFAM approach to fully automate the creation and evaluation of QM regions, including dynamic re-parametrization during molecular exploration.

Findings

Automated evaluation of QM region size and composition.

Elimination of dependence on pre-existing parameters for the classical part.

Efficient on-the-fly generation of classical parameters during molecular changes.

Abstract

We present a protocol for the fully automated construction of quantum mechanical-(QM)-classical hybrid models by extending our previously reported approach on self-parametrizing system-focused atomistic models (SFAM) J. Chem. Theory Comput. 2020, 16, 1646]. In this QM/SFAM approach, the size and composition of the QM region is evaluated in an automated manner based on first principles so that the hybrid model describes the atomic forces in the center of the QM region accurately. This entails the automated construction and evaluation of differently sized QM regions with a bearable computational overhead that needs to be paid for automated validation procedures. Applying SFAM for the classical part of the model eliminates any dependence on pre-existing parameters due to its system-focused quantum mechanically derived parametrization. Hence, QM/SFAM is capable of delivering a high fidelity and complete automation. Furthermore, since SFAM parameters are generated for the whole system, our ansatz allows for a convenient re-definition of the QM region during a molecular exploration. For this purpose, a local re-parametrization scheme is introduced, which efficiently generates additional classical parameters on the fly when new covalent bonds are formed (or broken) and moved to the classical region.