Charge and Exciton Transfer Simulations Using Machine-Learned Hamiltonians

Quantum-mechanical simulations of
charge and exciton transfer in
molecular organic materials are a key method to increase our understanding
of organic semiconductors. Our goal is to build an efficient multiscale
model to predict charge-transfer mobilities and exciton diffusion
constants from nonadiabatic molecular dynamics simulations and Marcus-based
Monte Carlo approaches. In this work, we apply machine learning models
to simulate charge and exciton propagation in organic semiconductors.
We show that kernel ridge regression models can be trained to predict
electronic and excitonic couplings from semiempirical density functional
tight binding (DFTB) reference data with very good accuracy. In simulations,
the models could reproduce hole mobilities along the anthracene crystal
axes to within 8.5% of the DFTB reference and 34% of the experimental
results with only 1000 training data points. Using these models decreased
the cost of exciton transfer simulations by one order of magnitude.