# Excitonic Wave Function Reconstruction from Near-Field Spectra Using Machine Learning Techniques

## TL;DR

This paper demonstrates that convolutional neural networks can effectively reconstruct excitonic wave functions from near-field spectra in molecular aggregates, aiding understanding of their optical and transport properties despite noise and disorder.

## Contribution

It introduces a machine learning approach to reconstruct high-dimensional excitonic wave functions from spectral data, overcoming limitations of traditional methods.

## Key findings

- Reconstruction is robust to disorder and noise.
- CNN successfully reconstructs wave functions in 1D and 2D aggregates.
- Method outperforms standard numerical approaches.

## Abstract

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.