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PreprintCC BY 4.0arXiv

Near-field maps of topological edge states of molecular aggregates

  • Aritra Mishra
  • Sidhartha Nayak
  • Alexander Eisfeld
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Abstract

Self-assembled two-dimensional molecular aggregates provide a versatile platform for engineering excitonic states, including topologically nontrivial edge states. However, these edge-states are difficult to probe optically: they have weak far-field transition strengths, and far-field spectra do not reveal whether an excitation is localized at the edge. For an aggregate consisting of two sublattices of molecules with genuinely complex transition dipoles, we show that spatially resolved near-field spectroscopy encodes the full spatial structure of these states. The resulting near-field maps do not mirror the site populations, but instead exhibit strongly nonuniform, interference-like features. We demonstrate that a convolutional neural network can reconstruct the complex expansion coefficients of the eigenstates from these maps, and that this reconstruction is robust against substantial random noise.

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Cite

BibTeXRIS
BibTeX
@misc{mishra2026nearfield,
  title = {{Near-field maps of topological edge states of molecular aggregates}},
  author = {Aritra Mishra and Sidhartha Nayak and Alexander Eisfeld},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.32287},
  eprint = {2610.32287},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.32287},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Near-field maps of topological edge states of molecular aggregates
AU  - Aritra Mishra
AU  - Sidhartha Nayak
AU  - Alexander Eisfeld
PY  - 2026
DA  - 2026/10/23
PB  - arXiv
DO  - 10.48550/arxiv.2610.32287
UR  - https://arxiv.org/abs/2610.32287
AB  - Self-assembled two-dimensional molecular aggregates provide a versatile platform for engineering excitonic states, including topologically nontrivial edge states. However, these edge-states are difficult to probe optically: they have weak far-field transition strengths, and far-field spectra do not reveal whether an excitation is localized at the edge. For an aggregate consisting of two sublattices of molecules with genuinely complex transition dipoles, we show that spatially resolved near-field spectroscopy encodes the full spatial structure of these states. The resulting near-field maps do not mirror the site populations, but instead exhibit strongly nonuniform, interference-like features. We demonstrate that a convolutional neural network can reconstruct the complex expansion coefficients of the eigenstates from these maps, and that this reconstruction is robust against substantial random noise.
ER  -