QmDFT for Polycyclic Aromatics: Balancing Embedding Ground-State Fidelity and Experimental Gap Estimation

• URL: http://arxiv.org/abs/2606.29245v1
• Date: Sun, 28 Jun 2026 07:27:53 GMT
• Title: QmDFT for Polycyclic Aromatics: Balancing Embedding Ground-State Fidelity and Experimental Gap Estimation
• Abstract summary: In this work, we introduce an adaptive damping and direct inversion in the iterative subspace (DIIS) accelerated protocol that stabilizes the embedding procedure.<n>We demonstrate a clear functional-dependent ground-state energetics and frontier-orbital gap estimation.<n>Our results establish a stable QmDFT framework and provide useful guidance for functional selection for quantum embedding studies of PAHs and related low-dimensional pi-conjugated materials.
• Score: 0.9772267314090435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum Embedding density functional theory (QmDFT) embedding offers a highly scalable approach to improve treatment for large highly correlated pi conjugated systems. However, estimating advanced electronic structure properties in polycyclic aromatic hydrocarbons (PAHs) needs advanced exchange correlation functionals that frequently trigger convergence instabilities during the embedding cycle. In this work, we introduce an adaptive damping and direct inversion in the iterative subspace (DIIS) accelerated protocol that stabilizes the embedding procedure, enabling robust integration of hybrid functionals like B3LYP and CAM B3LYP. Using 10 selected PAHs (linear and fused) molecules as a benchmark. We demonstrate a clear functional-dependent ground-state energetics and frontier-orbital gap estimation. While LDA based approaches yield near-quantitative agreement with FCI in DFT reference energies and is further supported by thermochemical isomerization benchmarks, while B3LYP provide significantly improved agreement with experimental E0-0 transition values. This mapping allows us to bypass explicit excited-state calculations for E0 0 values, thereby significantly reducing computational overhead. Among the hybrid functionals screened, CAM B3LYP offers a balanced overall performance. Our results establish a stable QmDFT framework and provide useful guidance for functional selection for quantum embedding studies of PAHs and related low-dimensional pi-conjugated materials.

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