Machine Learning of Two-Dimensional Spectroscopic Data

Abstract

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.

arXiv comment10 pages, 5 figures, 5 tables. Code and trained networks are available as ancillary files

References in corpus (11)

• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Machine learning phases of matter
• Learning phase transitions by confusion
• Machine Learning of Molecular Electronic Properties in Chemical Compound Space
• High-Performance Neural Networks for Visual Object Classification
• High-performance solution of hierarchical equations of motions for studying energy-transfer in light-harvesting complexes
• Modelling of Oscillations in Two-Dimensional Echo-Spectra of the Fenna-Matthews-Olson Complex
• Machine learning applied to single-shot x-ray diagnostics in an XFEL
• Prediction and real-time compensation of qubit decoherence via machine-learning
• Efficient calculation of open quantum system dynamics and time-resolved spectroscopy with Distributed Memory HEOM (DM-HEOM)

Cited by in corpus (6)

• Machine learning for excitation energy transfer dynamics
• Excitonic Wave Function Reconstruction from Near-Field Spectra Using Machine Learning Techniques
• Machine Learning Enabled Lineshape Analysis in Optical Two-Dimensional Coherent Spectroscopy
• Effect of disorder and polarization sequences on two-dimensional spectra of light harvesting complexes
• Modeling and simulating the excited-state dynamics of a system with condensed phases: A machine learning approach
• A machine learning approach to predict L-edge x-ray absorption spectra of light transition metal ion compounds