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首页> 外文期刊>Journal of chemical theory and computation: JCTC >Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation
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Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation

机译：通过机器学习在化学粘接解离式中的多引用方法自动化

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摘要

Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn-Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structures and chemical bonds of such systems. In the complete active space self-consistent field (CASSCF) method, one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. We present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a "black-box" mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.
机译：预测和理解化学键是计算量子化学的主要挑战之一。 Kohn-Maf密度功能理论（KS-DFT）是最常见的方法，但近似密度函数可能无法描述多种电子配置同样重要的系统。另一方面，多组件波函数可以详细了解这种系统的电子结构和化学键。在完整的主动空间自我一致的字段（Casscf）方法中，一个在由主动电子和有源轨道组成的活动空间中执行完全配置交互计算。但是，CASSCF及其变体需要选择这些活动空间。这个选择不是黑匣子;它需要用户的重大经验和测试，因此有源空间方法不被认为是特别用户友好的，并且仅通过少数昆腾化学家使用。我们的目标是通过使这些方法普及，使其更容易造成良好的积极空间选择。我们提出了一种机器学习协议，用于对主要组硅藻分子的化学粘合解算计算进行自动选择的有效空间。该协议为给定目标系统显示了高预测性能，只要选择正确相关的系统以进行训练。正确预测了良好的活动空间，比随机猜测相当更好的成功率（对于大多数学习的系统而言，大于80％的精确度）。我们的自动化机器学习协议表明，“黑匣子”模式可以促进和加速在多引用系统上的大规模计算，其中不能应用ks-dft等单引用方法。

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    Jeong WooSeok; Stoneburner Samuel J.; King Daniel; Li Ruye; Walker Andrew; Lindh Roland; Gagliardi Laura;

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    Univ Minnesota Dept Chem Nanoporous Mat Genome Ctr Minnesota Supercomp Inst Minneapolis MN 55455 USA;

    Univ Minnesota Dept Chem Nanoporous Mat Genome Ctr Minnesota Supercomp Inst Minneapolis MN 55455 USA;

    Univ Minnesota Dept Chem Nanoporous Mat Genome Ctr Minnesota Supercomp Inst Minneapolis MN 55455 USA;

    Univ Minnesota Dept Chem Nanoporous Mat Genome Ctr Minnesota Supercomp Inst Minneapolis MN 55455 USA;

    Univ Minnesota Dept Comp Sci &

    Engn Minneapolis MN 55455 USA;

    Uppsala Univ Dept Chem BMC S-75123 Uppsala Sweden;

    Univ Minnesota Dept Chem Nanoporous Mat Genome Ctr Minnesota Supercomp Inst Minneapolis MN 55455 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 化学键的量子力学理论;化学;
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