通用纳米级应用的通用QM/MM方法

IF 16.8 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Katja-Sophia Csizi, Markus Reiher
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引用次数: 5

摘要

量子力学/分子力学(QM/MM)混合模型允许人们在复杂的分子环境中解决化学现象。尽管这种建模方法可以以中等的计算成本处理大型系统,但模型的构建通常很繁琐,需要手工预处理和专业知识。因此,向新应用领域的可转移性可能受到限制,并且许多参数不容易调整为通常稀缺的参考数据。因此,需要设计出精度可控的自动化过程,使这种建模能够以标准化和黑盒类型的方式进行。尽管已经为构建QM/MM模型的各个组件建立了不同的最佳实践协议(例如,MM潜力、嵌入类型、QM区域的选择),但是协调QM/MM模型构建的所有步骤的自动化过程仍然很少。在这里,我们以自动化为重点回顾QM/MM建模技术的现状。我们详细阐述了MM模型的参数化,原子经济物理驱动的QM区域选择,以及将互极化作为QM/MM模型的关键组成部分的嵌入方案。鉴于该领域的广泛范围,我们主要将讨论限制在基于第一性原理数据、不确定性量化和具有高自动化潜力的错误缓解的基础上建立从头模型的方法上。最终,希望能够以快速有效的自动化方式建立可靠的QM/MM模型,而不受特定化学或技术限制的约束。本文分类如下:
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Universal QM/MM approaches for general nanoscale applications

Universal QM/MM approaches for general nanoscale applications

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 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.

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来源期刊
Wiley Interdisciplinary Reviews: Computational Molecular Science
Wiley Interdisciplinary Reviews: Computational Molecular Science CHEMISTRY, MULTIDISCIPLINARY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
28.90
自引率
1.80%
发文量
52
审稿时长
6-12 weeks
期刊介绍: Computational molecular sciences harness the power of rigorous chemical and physical theories, employing computer-based modeling, specialized hardware, software development, algorithm design, and database management to explore and illuminate every facet of molecular sciences. These interdisciplinary approaches form a bridge between chemistry, biology, and materials sciences, establishing connections with adjacent application-driven fields in both chemistry and biology. WIREs Computational Molecular Science stands as a platform to comprehensively review and spotlight research from these dynamic and interconnected fields.
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