Charge and Exciton Transfer Simulations Using Machine-Learned Hamiltonians
datasetposted on 18.06.2020, 17:07 by Mila Krämer, Philipp M. Dohmen, Weiwei Xie, Daniel Holub, Anders S. Christensen, Marcus Elstner
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.
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reference dataexciton propagationDFTB referenceMachine-Learned Hamiltonians Quantum-mechanical simulationssemiconductorkernel ridge regression modelshole mobilities1000 training data pointsExciton Transfer SimulationsMarcus-based Monte Carlo approachesexciton transfer simulationscharge-transfer mobilitiesmultiscale modeldynamics simulationsexcitonic couplingsexciton transferanthracene crystal axesexciton diffusion constants