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量子-经典混合模型的自动化构建。

Automated Construction of Quantum-Classical Hybrid Models.

机构信息

Laboratorium für Physikalische Chemie, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland.

出版信息

J Chem Theory Comput. 2021 Jun 8;17(6):3797-3813. doi: 10.1021/acs.jctc.1c00178. Epub 2021 May 12.

DOI:10.1021/acs.jctc.1c00178
PMID:33979514
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 (SFAMs) [Brunken, C.; Reiher, M. 2020, 16, 3, 1646-1665]. In this QM/SFAM approach, the size and composition of the QM region are 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 high fidelity and complete automation. Furthermore, since SFAM parameters are generated for the whole system, our ansatz allows for a convenient redefinition of the QM region during a molecular exploration. For this purpose, a local reparametrization 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.

摘要

我们提出了一种通过扩展我们之前关于自参数化系统聚焦原子模型 (SFAM) 的报告[Brunken, C.; Reiher, M. 2020, 16, 3, 1646-1665]来自动构建量子力学 (QM)-经典混合模型的协议。在这种 QM/SFAM 方法中,QM 区域的大小和组成根据第一性原理以自动方式进行评估,以便混合模型准确描述 QM 区域中心的原子力。这需要支付自动化验证过程的可承受计算开销来自动构建和评估具有不同大小的 QM 区域。由于其系统聚焦的量子力学衍生参数化,SFAM 应用于模型的经典部分消除了对任何预先存在参数的依赖。因此,QM/SFAM 能够提供高保真度和完全自动化。此外,由于 SFAM 参数是为整个系统生成的,我们的假设允许在分子探索期间方便地重新定义 QM 区域。为此,引入了局部重新参数化方案,当形成(或打破)新的共价键并移动到经典区域时,该方案可有效地即时生成额外的经典参数。

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