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Q 29.2: Vortrag
Mittwoch, 11. März 2020, 11:15–11:30, f442
Excitonic Wave Function Reconstruction from Near-Field Spectra Using Machine Learning Techniques — •Fulu Zheng1, Xing Gao1,2, and Alexander Eisfeld1 — 1Max-Planck-Institut für Physik komplexer Systeme, Nöthnitzer Strasse 38, D-01187 Dresden, Germany — 2Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109-1055, USA
A general problem in quantum mechanics is the reconstruction of eigenstate wave functions from measured data. Self-assembled molecular aggregates on dielectric surfaces are promising candidates for optoelectronic devices. Strong interactions between the transition dipoles of the molecules lead to delocalized excitonic eigenstates where an electronic excitation is coherently shared by many molecules . Information about these states 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 [2, 3]. 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.
 A. Eisfeld, C. Marquardt, A. Paulheim, and M. Sokolowski, Phys. Rev. Lett. 119, 097402 (2017).  X. Gao and A. Eisfeld, J. Phys. Chem. Lett. 9, 6003 (2018).  F. Zheng, X. Gao and A. Eisfeld, Phys. Rev. Lett. 123, 163202 (2019).