SKM 2023 – wissenschaftliches Programm

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DY: Fachverband Dynamik und Statistische Physik

DY 43: Poster: Quantum Dynamics and Many-Body Systems

DY 43.18: Poster

Donnerstag, 30. März 2023, 13:00–16:00, P1

Classification of noisy spectra using machine learningAritra Mishra and •Alexander Eisfeld — Max Planck Institute for the Physics of Complex Systems, Dresden, Germany

A general problem in quantum mechanics is to obtain information of the eigenstates from the experimentally measured data which consists inherent noises. For an example, in the case of molecular aggregates, the information about excitonic eigenstates is vitally important to understand their optical and transport properties [1,2].

We show that it is possible to reconstruct the underlying delocalised aggregate eigenfunctions from near-field spectra using convolution neural networks [3]. We also investigate convolution neural networks for an eigenstate based classification of the spectra, in the presence of noise. Each aggregate eigenstate, corresponds to a distinctly looking spectrum. Therefore, we can assign a class to each of the eigenstate. We find that the network is also able to classify the spectra of different noise strengths along with the one it has been trained for.

[1] X. Gao and A. Eisfeld, J. Phys. Chem. Lett. 9, 6003 (2018)
[2] S. Nayak, F. Zheng and A. Eisfeld, J. Chem. Phys. 155, 134701 (2021)
[3] F. Zheng, X. Gao and A. Eisfeld, Phys. Rev. Lett. 123, 163202 (2019)

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