Trends in Chemistry
Volume 2, Issue 6, June 2020, Pages 535-554
Journal home page for Trends in Chemistry

Review
Organic Photovoltaics: Relating Chemical Structure, Local Morphology, and Electronic Properties

https://doi.org/10.1016/j.trechm.2020.03.006Get rights and content

Highlights

  • Robust computational methodologies that combine all-atom molecular dynamics simulations and density functional theory calculations allow a molecular-scale description of chemical structure–local morphology–electronic properties relationships.

  • Packing of polymer chain segments on molecules at the nanoscale is found to depend on the strength of intermolecular interactions, the degree of molecular planarity, and the steric distribution of side chains.

  • As an overall measure of intermolecular interactions present in a blend, the Flory–Huggins interaction parameter, χ, can be used to evaluate the degree of intermolecular mixing or the extent of phase separation.

  • The reduced voltage losses measured in efficient polymer:NF-SMA systems can be attributed to higher energies of the charge-transfer electronic states, lower energetic disorders, and greater luminescence efficiency.

  • Coarse-graining methods are expected to play a critical role in paving the way from local morphology to global morphology.

Substantial enhancements in the efficiencies of bulk-heterojunction (BHJ) organic solar cells (OSCs) have come from largely trial-and-error-based optimizations of the morphology of the active layers. Further improvements, however, require a detailed understanding of the relationships among chemical structure, morphology, electronic properties, and device performance. On the experimental side, characterization of the local (i.e., nanoscale) morphology remains challenging, which has called for the development of robust computational methodologies that can reliably address those aspects. In this review, we describe how a methodology that combines all-atom molecular dynamics (AA-MD) simulations with density functional theory (DFT) calculations allows the establishment of chemical structure–local morphology–electronic properties relationships. We also provide a brief overview of coarse-graining methods in an effort to bridge local to global (i.e., mesoscale to microscale) morphology. Finally, we give a few examples of machine learning (ML) applications that can assist in the discovery of these relationships.

Section snippets

Active-Layer Morphology Impacts Device Performance

Since the BHJ architecture was introduced in the mid-1990s [1,2], OSCs have witnessed substantial improvements in their power conversion efficiencies (PCEs). The active layer of a BHJ OSC is formed by a blend of an electron-donor and an electron-acceptor material, with the two components mixing down to the ~10 nm scale; Figure 1A illustrates a typical polymer:fullerene BHJ architecture with the basic electronic processes described in the legend [3] (the chemical structures of the donor and

Correlating Chemical Structure with Local Morphology by AA-MD Simulations

The output of an AA-MD simulation is an atomistic trajectory from which the local morphology (i.e., intermolecular packing and mixing) can be analyzed, for instance, by examining radial distribution functions (RDFs) among donors, among acceptors, or between donors and acceptors. Note that due to the size limitation of AA-MD simulations, the various regions of an active layer have to be modeled separately (i.e., the pure phases via a neat film and mixed phases via a blend). Recent studies have

Correlating Local Morphology to Electronic Properties via DFT Calculations

From the AA-MD outputs, intramolecular conformations and intermolecular configurations can be extracted; the former relate to single donor or acceptor macromolecules, while the latter comprise donor/donor, donor/acceptor, or acceptor/acceptor pairs. On extracting a large set (hundreds or thousands) of single donor (acceptor) molecules, their site energies and disorder relevant for hole (electron) transport can be evaluated via DFT calculations; here, a small disorder means more efficient charge

From Local to Global Morphology

As mentioned earlier, global morphology still largely remains to be experimentally explored in the case of efficient non-fullerene-based OSCs, although it is known to influence device performance [15,49]. This is thus an area where CG-MD simulations can play a useful role since they significantly reduce the degrees of freedom involved in the modeling and allow one to address greater system sizes and timescales and to connect local morphology to global morphology. Ultimately, the objective is to

Opportunities for ML

In recent years, artificial intelligence (AI) has started to play an increasing role in organic photovoltaics (OPVs). In particular, studies have focused on the use of ML to screen OPV materials based on molecular descriptors such as molecular level (highest occupied molecular orbital or lowest unoccupied molecular orbital) energies, band gaps, and chemical features (e.g., atom and bond types, connectivity) [90., 91., 92., 93., 94.]. These large-scale screenings aim ultimately to make a

Concluding Remarks and Future Perspectives

Recent computational studies based on a tight combination of AA-MD simulations and DFT calculations have enhanced our understanding of the connections among chemical structure, local morphology, and electronic properties in BHJ OSCs. However, since the investigations conducted to date have been limited to just a few systems, a comprehensive picture is still lacking. This is especially true since the effects from, for instance, solvents, additives, molecular weights, or fabrication processing

Acknowledgments

This work has been supported by the Office of Naval Research in the framework of Awards No. N00014-17-1-2208 and No. N00014-20-1-2110, as well as by the Georgia Institute of Technology and the University of Arizona. We acknowledge many stimulating discussions with Dr Veaceslav Coropceanu.

Glossary

Atomistic trajectory
trajectory that includes the coordinates of all of the atoms in a system as a function of simulation time.
Charge-transfer (CT) electronic states
occur at the donor–acceptor interfaces and correspond to a full or partial electron transfer from an electron donor to an acceptor on photoexcitation.
Donor/acceptor pair
comprises a donor and an acceptor directly interacting within a given distance of each other; the cutoff distance along the interaction direction is based on the

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