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Single Particle Spectrum of Doped $\mathrm{C}_{20}\mathrm{H}_{12}$-Perylene

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arxiv 2406.06711 v1 pith:PTUWI344 submitted 2024-06-10 cond-mat.str-el hep-lat

Marcel Rodekamp , Evan Berkowitz , Christoph Gäntgen , Stefan Krieg , Thomas Luu , Johann Ostmeyer , Giovanni Pederiva This is my paper · ORCID
classification cond-mat.str-elhep-lat
keywords mathrmchemicaldopedperyleneorganicpotentialspectrumacceptor
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abstract

We present a Hamiltonian Monte Carlo study of doped perylene $\mathrm{C}_{20}\mathrm{H}_{12}$ described with the Hubbard model. Doped perylene can be used for organic light-emitting diodes (OLEDs) or as acceptor material in organic solar cells. Therefore, central to this study is a scan over charge chemical potential. A variational basis of operators allows for the extraction of the single-particle spectrum through a mostly automatic fitting procedure. Finite chemical potential simulations suffer from a sign problem which we ameliorate through contour deformation. The on-site interaction is kept at $U/\kappa = 2$. Discretization effects are handled through a continuum limit extrapolation. Our first-principles calculation shows significant deviation from non-interacting results especially at large chemical potentials.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Search for Stable States in Two-Body Excitations of the Hubbard Model on the Honeycomb Lattice

    cond-mat.str-el 2025-02 conditional novelty 6.0 of 10

    Quantum Monte Carlo simulations on an 18-site honeycomb lattice at U=3.0 find negative two-body energy shifts in the charge-zero channel, indicating attraction but not conclusively a bound state.

  2. Exploring Group Convolutional Networks for Sign Problem Mitigation via Contour Deformation

    cond-mat.dis-nn 2025-02 conditional novelty 5.0 of 10

    Group convolutional networks with built-in lattice symmetries outperform fully connected networks for learned contour deformations in small Hubbard-model sign-problem simulations, but transfer learning across paramete...

  3. Machine-learning approaches to accelerating lattice simulations

    hep-lat 2025-02 unverdicted

    A review of unbiased machine-learning acceleration methods for lattice field theory, covering flow-based sampling, contour deformations, control variates, and surrogate observables.

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