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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 artifacts at the interface. Furthermore, they are still limited by high computational costs of quantum chemical calculations. In this work, we develop the buffer region neural network (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 artifacts. The interactions between the QM and the buffer region are described by deep neural networks (NNs), which leads to the 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.

A series of novel hetero­[n]­circulenes of the first, second, and the third generation including S and Se heteroatoms for n = 6, 7, 8, and 9 were theoretically designed. Their chemical and electronic structures were investigated using B3LYP-based computational method. The interaction energies and electric drift mobilities were evaluated for model parallel-stacked and parallel-slipped dimer configurations at the room temperature using the Marcus theory and the Einstein relation. On the basis of the calculated properties, the second and third generation of sunflower molecules containing the six-membered central ring are suggested to be perspective candidates for the construction of organic p- and n-type semiconductors, respectively. The obtained results were also compared with the theoretical and published experimental results for reference α-sexithiophene, [6]­circulene (coronene), and octathio[8]­circulene (sulflower) molecules.

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