Dye-sensitized solar cells (DSSCs) are one of the most promising renewable energy technologies. Charge transfer and charge transport are pivotal processes in DSSCs, which govern solar energy capture and conversion. These processes can be probed using modern electronic structure methods. Because of the heterogeneity and complexity of the local environment of a chromophore in DSSCs (such as solvatochromism and chromophore aggregation), a part of the solvation environment should be treated explicitly during the calculation. However, because of the high computational cost and unfavorable scaling with the number of electrons of high-level quantum mechanical methods, approaches to explicitly treat the local environment need careful consideration. Two problems must be tackled to reduce computational cost. First, the number of configurations representing the solvent distribution should be limited as much as possible. Second, the size of the explicit region should be kept relatively small. The purpose of this study is to develop efficient computational approaches to select representative configurations and to limit the explicit solvent region to reduce the computational cost for later (higher-level) quantum mechanical calculations. For this purpose, an ensemble of solvent configurations around a 1-methyl-8-oxyquinolinium betaine (QB) dye molecule was generated using Monte Carlo simulations and molecular mechanics force fields. Then, a fitness function was developed using data from inexpensive electronic structure calculations to reduce the number of configurations. Specific solvent molecules were also selected for explicit treatment based on a distance criterion, and those not selected were treated as background charges. The configurations and solvent molecules selected proved to be good representatives of the entire ensemble; thus, expensive electronic structure calculations need to be performed only on this subset of the system, which significantly reduces the computational cost.
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Acknowledgment: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program, under Award No. DE-SC0008666. The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing computational resources that contributed to this work.
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- Monte carlo simulation