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

被引:13
|
作者
Zheng, Fulu [1 ]
Gao, Xing [1 ,2 ]
Eisfeld, Alexander [1 ]
机构
[1] Max Planck Inst Phys Komplexer Syst, Nothnitzer Str 38, D-01187 Dresden, Germany
[2] Univ Michigan, Dept Chem, Ann Arbor, MI 48109 USA
关键词
SPECTROSCOPY; MICROSCOPY; TIP;
D O I
10.1103/PhysRevLett.123.163202
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
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.
引用
收藏
页数:5
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