Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Excitonic Wave Function Reconstruction from Near-Field Spectra Using
  Machine Learning Techniques   A general problem in quantum mechanics is the reconstruction of eigenstate
wave functions from measured data. In the case of molecular aggregates,
information about excitonic eigenstates is vitally important to understand
their optical and transport properties. Here we show that from spatially
resolved near field spectra it is possible to reconstruct the underlying
delocalized aggregate eigenfunctions. Although this high-dimensional nonlinear
problem defies standard numerical or analytical approaches, we have found that
it can be solved using a convolutional neural network. For both one-dimensional
and two-dimensional aggregates we find that the reconstruction is robust to
various types of disorder and noise.

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The categories of this paper are: comp-gas
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
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The categories of this paper are: supr-con
1
The categories of this paper are: cs
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The categories of this paper are: adap-org
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The categories of this paper are: math-ph