AI 中文总结
本研究提出TNASS方法，将轨道划分纯度表示为矩阵乘积态以分离强关联电子，无需手动预选或高阶密度矩阵计算，其基态能量更低、偶极矩更准，可用于分子电子结构计算的活性空间选择。
AI 中文摘要
分子电子结构计算中的多尺度建模技术（如嵌入方法和子空间方法）的质量依赖于所选的活性空间，活性空间选择的自动化对确保此类计算的准确性、可重复性和可扩展性至关重要。本研究提出了基于纠缠特征的张量网络活性空间选择（Tensor Network Active Space Selection using the Entanglement Feature，简称TNASS）方法，通过分离强关联电子，为多尺度建模中的嵌入方法提供了可扩展的基础。该方法将所有可能轨道划分的纯度表示为矩阵乘积态（Matrix Product State），无需手动预选目标原子或计算昂贵的高阶密度矩阵，即可分离强电子关联区域。实验结果表明，与其他完全自动化选择方案（如仅基于单轨道熵或选择HOMO/LUMO间隙周围空间轨道的方案）相比，该方法能得到更低的基态能量和更准确的偶极矩。
英文摘要
The quality of multi-scale modelling techniques in molecular electronic structure calculations, such as embedding and subspace methods, relies upon the chosen active space. The automation of active space selection is vital for ensuring the accuracy, reproducibility, and scalability in such calculations. In this work, we introduce Tensor Network Active Space Selection using the Entanglement Feature. Through the isolation of strongly correlated electrons, this method provides a scalable foundation for embedding methods in multi-scale modelling. By representing the purities of all possible orbital partitions as a Matrix Product State, our method isolates regions of strong electron correlation without requiring manual preselection of target atoms or the calculation of expensive high-order density matrices. The results demonstrate that this approach leads to lower ground state energies and more accurate dipole moments than other fully automated selection schemes such as those based solely on single-orbital entropy or the selection of spatial orbitals around the HOMO/LUMO gap.
