Machine Learning Exciton Dynamics

TL;DR

This paper introduces a machine learning approach using multi-layer perceptrons to efficiently predict excited state energies in large photosynthetic complexes, significantly reducing computation time while maintaining high accuracy.

Contribution

The study demonstrates that multi-layer perceptrons can accurately predict TDDFT excited state energies, spectral densities, and exciton populations, accelerating quantum chemistry calculations.

Findings

Prediction errors within 0.01 eV (0.5%)

Significant speed-up over QM/MM calculations

High agreement of spectral densities and exciton dynamics with TDDFT

Abstract

Obtaining the exciton dynamics of large photosynthetic complexes by using mixed quantum mechanics/molecular mechanics (QM/MM) is computationally demanding. We propose a machine learning technique, multi-layer perceptrons, as a tool to reduce the time required to compute excited state energies. With this approach we predict time-dependent density functional theory (TDDFT) excited state energies of bacteriochlorophylls in the Fenna-Matthews-Olson (FMO) complex. Additionally we compute spectral densities and exciton populations from the predictions. Different methods to determine multi-layer perceptron training sets are introduced, leading to several initial data selections. In addition, we compute spectral densities and exciton populations. Once multi-layer perceptrons are trained, predicting excited state energies was found to be significantly faster than the corresponding QM/MM calculations. We showed that multi-layer perceptrons can successfully reproduce the energies of QM/MM calculations to a high degree of accuracy with prediction errors contained within 0.01 eV (0.5%). Spectral densities and exciton dynamics are also in agreement with the TDDFT results. The acceleration and accurate prediction of dynamics strongly encourage the combination of machine learning techniques with ab-initio methods.