Supporting data for "Development of Downfolded Configuration Interaction algorithm for molecular electronic structure calculation and computation chemistry application"

Accurately calculating the electronic structure of strongly correlated molecular systems, such as transition metal complexes, actinides, lanthanides, and extended $\pi$-conjugated organic molecules, is a significant challenge in
the field of quantum chemistry. In these systems, strong electron correlations are crucial, and their accurate description is essential for predicting and understanding the properties and reactivity, which includes but is not limited to bond breaking, charge transfer, and excited states.

Traditional methods often have difficulties in accurately capturing the intricate electron correlations in these
systems, which limits their capacity to produce reliable results. Furthermore, the scaling of high-level methods has significant limitations on their practical use when the system correlation size becomes large, making them computationally prohibitive. As a result, there is a need to develop efficient approximate techniques that can maintain high accuracy while reducing computational cost.

In an exact diagonalization of benzene in the cc-pVDZ basis, the Hilbert space contains over $10^{45}$ determinants. However, the molecular Hamiltonian in real systems is very sparse, with the vast majority of matrix elements being negligible. This sparsity can be exploited to reduce the effective dimension dramatically.

This dataset presents the development and application of the Downfolded Configuration Interaction (dCI) algorithm for accurate and efficient calculations of molecules with strong correlations. The dCI, as a branch of the selected CI method, constructs a compact, effective Hamiltonian through an iterative process that captures the essential electron correlations. This is accomplished by carefully selecting significant configurations and applying a local treatment to the model subspace. Building upon the dCI algorithm, the extended-dCI (ext-dCI) method has been developed to further increase the applicability to larger molecular systems by approximating effective Hamiltonian expansion truncation.

Detailed benchmarks demonstrate the accuracy and efficiency of ext-dCI in calculating challenging systems such as benzene and chromium dimer. Utilizing the sparsity and locality of the electronic structure, the dCI and ext-dCI methods provide an in-depth description of both static and dynamic electron correlation effects. The dCI approach opens new possibilities for studying complex chemical phenomena governed by strong electron correlations.

Some computational works on supramolecular and $\pi$-carbon chemistry during the candidature period were also included.

This data set contains several computational projects, including the data from Gaussian/orca/ibo/pyscf/dCI together with other plots.

精确计算强关联分子系统（如过渡金属配合物、锕系元素、镧系元素以及扩展π共轭有机分子）的电子结构，是量子化学领域的一项重大挑战。在这些体系中，强电子关联至关重要，对其精确描述对于预测和理解其性质与反应性至关重要，这包括但不限于键断裂、电荷转移和激发态。
传统方法往往难以精确捕捉这些体系中的复杂电子关联，这限制了它们产生可靠结果的能力。此外，当体系关联尺寸增大时，高级方法的扩展性存在显著限制，使得其实际应用变得计算上难以承受。因此，迫切需要开发高效的近似技术，在保持高精度的同时降低计算成本。
在苯的cc-pVDZ基底的精确对角化中，希尔伯特空间包含超过$10^{45}$个行列式。然而，真实系统中的分子哈密顿矩阵非常稀疏，其中绝大多数矩阵元素可以忽略不计。这种稀疏性可以被利用以大幅降低有效维度。
本数据集展示了Downfolded Configuration Interaction（dCI）算法的开发和应用，该算法用于对具有强关联的分子进行精确且高效的计算。作为选择配置相互作用方法的一个分支，dCI通过迭代过程构建一个紧凑、有效的哈密顿矩阵，从而捕捉关键的电子关联。这是通过精心选择显著配置并对模型子空间进行局部处理来实现的。在dCI算法的基础上，扩展-dCI（ext-dCI）方法已被开发出来，通过近似有效哈密顿矩阵的截断扩展，进一步增加了对更大分子体系的适用性。
详细的基准测试展示了ext-dCI在计算诸如苯和铬二聚体等挑战性体系中的准确性和效率。利用电子结构的稀疏性和局部性，dCI和ext-dCI方法提供了对静态和动态电子关联效应的深入描述。dCI方法为研究受强电子关联支配的复杂化学现象开辟了新的可能性。
候选期间在超分子和π-碳化学方面的某些计算工作也被包含在内。
本数据集包含多个计算项目，包括Gaussian/orca/ibo/pyscf/dCI的数据以及其他图表。