BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network / Molecular Mechanics Simulations

TL;DR

BuRNN is a neural network-based hybrid simulation method that models a buffer region with full electronic polarization to reduce interface artefacts and improve computational efficiency in QM/MM simulations.

Contribution

It introduces a buffer region with neural network modeling to enhance accuracy and efficiency in hybrid quantum/classical molecular simulations.

Findings

Successfully applied to hexa-aqua iron complex

Reduces artefacts at QM/MM interface

Maintains quantum chemical accuracy

Abstract

Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have advanced the field of computational chemistry tremendously. However, they require the partitioning of a system into two different regions that are treated at different levels of theory, which can cause artefacts at the interface. Furthermore, they are still limited by high computational costs of quantum chemical calculations. In this work, we develop BuRNN, an alternative approach to existing QM/MM schemes, which introduces a buffer region that experiences full electronic polarization by the inner QM region to minimize artefacts. The interactions between the QM and the buffer region are described by deep neural networks (NNs), which leads to high computational efficiency of this hybrid NN/MM scheme while retaining quantum chemical accuracy. We demonstrate the BuRNN approach by performing NN/MM simulations of the hexa-aqua iron complex.