高自旋分子中g矩阵分析的有效态相互作用方法

IF 2.9 3区 化学 Q3 CHEMISTRY, PHYSICAL
Antonio Cebreiro-Gallardo and David Casanova
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引用次数: 0

摘要

我们提出了一种有效的状态相互作用方法来评估高自旋分子系统中的g位移。该方法利用自旋-轨道耦合有效哈密顿量和受限的主动空间组态相互作用波函数,在不需要大的轨道空间的情况下捕获了g位移的关键激发态贡献,保持了计算效率。此外,我们还引入了一种属性驱动算法来自动选择相关轨道,优化活动空间的选择。应用于双原子和共轭有机分子证明了与先进方法相当的准确性,提供了对g位移起源的详细见解。这种方法为探索复杂分子的磁性提供了一种灵活、有效的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Efficient state-interaction approach for the g-matrix analysis in high-spin molecules†

Efficient state-interaction approach for the g-matrix analysis in high-spin molecules†

We present an efficient state-interaction approach for evaluating g-shifts in high-spin molecular systems. Using a spin–orbit-coupled effective Hamiltonian with a restricted active space configuration interaction wavefunction, this method captures key excited-state contributions to g-shifts without requiring large orbital spaces, maintaining computational efficiency. Additionally, we introduce a property-driven algorithm to automatically select relevant orbitals, optimizing the active space selection. Application to diatomic and conjugated organic molecules demonstrates accuracy comparable to advanced methods, providing detailed insight into the origins of g-shifts. This methodology offers a flexible, efficient tool for exploring magnetic properties in complex molecules.

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来源期刊
Physical Chemistry Chemical Physics
Physical Chemistry Chemical Physics 化学-物理:原子、分子和化学物理
CiteScore
5.50
自引率
9.10%
发文量
2675
审稿时长
2.0 months
期刊介绍: Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.
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