期刊
CHEMICAL PHYSICS
卷 520, 期 -, 页码 52-60出版社
ELSEVIER
DOI: 10.1016/j.chemphys.2019.01.002
关键词
Excitonic energy transfer; Light-harvesting complexes; ML numerical methods; Neural networks
资金
- North-German Supercomputing Alliance (HLRN)
- German Research Foundation (DFG) [KR 2889, RE 1389]
- European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant [707636]
- Marie Curie Actions (MSCA) [707636] Funding Source: Marie Curie Actions (MSCA)
Two-dimensional electronic spectroscopy has become one of the main experimental tools for analyzing the dynamics of excitonic energy transfer in large molecular complexes. Simplified theoretical models are usually employed to extract model parameters from the experimental spectral data. Here we show that computationally expensive but exact theoretical methods encoded into a neural network can be used to extract model parameters and infer structural information such as dipole orientation from two dimensional electronic spectra (2DES) or reversely, to produce 2DES from model parameters. We propose to use machine learning as a tool to predict unknown parameters in the models underlying recorded spectra and as a way to encode computationally expensive numerical methods into efficient prediction tools. We showcase the use of a trained neural network to efficiently compute disordered averaged spectra and demonstrate that disorder averaging has non-trivial effects for polarization controlled 2DES.
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