Computationally connecting organic photovoltaic performance to atomistic arrangements and bulk morphology

Abstract

Rationally designing roll-to-roll printed organic photovoltaics (OPVs) requires a fundamental understanding of active layer morphologies optimised for charge separation and transport, and which ingredients can be used to self-assemble those morphologies. In this review article we discuss advances in three areas of computational modelling that provide insight into active layer morphology and the charge transport properties that result. We explain the computational bottlenecks prohibiting atomistically-detailed simulations of device-scale active layers and the coarse-graining and hardware acceleration strategies for overcoming them. We review coarse-grained simulations of OPV active layers and show that high throughput simulations of experimentally-relevant length scales are now accessible. We describe a new Python package diffractometer that permits grazing-incidence X-ray scattering patterns of simulated active layers to be compared against experiments. We explain the accurate calculation of charge-carrier mobilities from coarse-grained active layer morphologies by using atomistic backmapping, quantum chemical calculations, and kinetic Monte Carlo simulations. We employ these simulations to show that ordering of poly(3-hexylthiophene-2,5-diyl) explains a factor of 1000 improvement in charge mobility. In concert, we present a suite of computational tools enabling large-scale electronic properties of organic photovoltaics to be studied and screened for by molecular simulations.

Keywords:

• Organic photovoltaics
• molecular dynamics
• coarse-grained
• fine-graining
• atomistic backmapping
• charge mobility
• diffraction

Notes

No potential conflict of interest was reported by the authors.

Supplemental data for this article can be accessed http://dx.doi/10.1080/08927022.2017.1296958.

Additional information

Funding

This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation [grant number ACI-1053575] [155]. This material is based upon work supported by the National Science Foundation [grant number 1229709].