基于机器学习改进的密度矩阵重整化群计算实现快速准确的激发能
Fast and Accurate Excitation Energies from Density Matrix Renormalization Group Calculations Improved by Machine Learning
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中文总结 AI 辅助
本文提出结合密度矩阵重整化群与机器学习的方法，快速准确计算功能材料激发能，并在多达34个π电子的多环芳香化合物上验证了模型的迁移性和有效性。
中文摘要 AI 辅助
在现代功能材料科学中，光电子性质的估算是至关重要的需求，同时也是一项持续的挑战。这涉及在具有复杂电子结构的系统中准确获取基态和激发态，而这些系统对于精确的多体方法而言往往计算成本过高。基于我们先前关于基态的研究工作[J. Phys. Chem. Lett. 2025, 16, 3295-3301]，我们提出了一种高效且经济的方法来评估功能材料中的电子激发，该方法将密度矩阵重整化群方法作为完全活性空间求解器与机器学习技术相结合。我们展示了该方法在π电子相关系统（即多环芳香化合物）上的性能。所得到的机器学习模型的迁移性和有效性在多达34个π电子的多个具有挑战性的实例中得到了验证。
英文摘要
An estimation of optoelectronic properties is a crucial necessity and an ongoing challenge in modern functional material science. This involves accurate accessing of ground and excited states in systems with complex electronic structure that are often too computationally expensive for accurate many-body methods. Building on our previous work on ground states [J. Phys. Chem. Lett. 2025, 16, 3295-3301], we present an efficient and cost-effective approach for evaluating electronic excitations in functional materials, combining the density matrix renormalization group method as a complete active space solver with machine learning techniques. We demonstrate its performance on π-electron-correlated systems, namely polycyclic aromatic compounds. The transferability and effectiveness of the derived machine learning model are demonstrated on a number of challenging examples with up to 34 π-electrons.
