Computational Evolution of High-Performing Unfused Non-Fullerene Acceptors for Organic Solar Cells

Abstract

Materials optimization for organic solar cells (OSCs) is a highly active field, with many approaches using empirical experimental synthesis, computational brute-force approaches to screen candidates in a given subset of chemical space, or generative machine learning methods which often require significant training sets. While these methods may find high- performing materials, they can be inefficient and time-consuming. Genetic algorithms (GAs) are an alternative approach, allowing for the "virtual synthesis" of molecules and a prediction of their “fitness” for some property, with new candidates suggested based on good characteristics of previously generated molecules. In this work, a GA is used to discover high-performing unfused non-fullerene acceptors (NFAs) based on an empir- ical prediction of power conversion efficiency (PCE) and provides design rules for future work. The electron withdrawing/donating strength, as well as the sequence and symmetry of those units are examined. The utilization of a GA over a brute force approach resulted in speedups up to 1.8 × 1012. New types of units not frequently seen in OSCs are suggested, and in total 5,426 NFAs are discovered with the GA. Of these, 1,087 NFAs are predicted to have a PCE greater than 18%, which is roughly the current record efficiency. While the symmetry of the sequence showed no correlation with PCE, analysis of the sequence arrangement revealed that higher performance can be achieved with a donor core and ac- ceptor end groups. Future NFA designs should consider this strategy as an alternative to the current A-D-A′-D-A architecture.

Artificial Intelligence for Conjugated Polymers

“…93,94 Similar machine learning techniques have been employed on these combinations to pursue the tandem solar cells with 20% efficiency. 95,96 Advancing prediction accuracy in polymer photovoltaics through a comprehensive public experimental database, a combinatory search of polymer donors and small molecule acceptors, and the improvement of descriptors is considered a noteworthy advancement. While the current data sets have limitations, the application of random forest shows promise; a large data set might give opportunities to neural network methods.…”

Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells

“…Hutchison et al 134 discovered novel NFAs by screening 5426 NFAs using a genetic algorithm and various sequences, symmetry and building blocks were analyzed. PBDB-T-SF is used as the donor material for this study because of its strong absorption in UV and high-energy visible regions.…”

Novel A-π-D-π-A type non-fullerene acceptors of dithienyl diketopyrropopyrrole derivatives to enhance organic photovoltaic applications: a DFT study

“…17,18 In this regard, un-fused NFAs are acting as the premier contributors due to their two- to four-step and cost-effective synthetic routes, as well as high yield as opposed to their counterparts, the fused NFAs. 19…”

“…17,18 In this regard, un-fused NFAs are acting as the premier contributors due to their two-to four-step and cost-effective synthetic routes, as well as high yield as opposed to their counterparts, the fused NFAs. 19 The TDPP-RDN compound has already been engineered and scrutinized to evaluate the effectiveness and performance of OPVs. 20 We chose this compound as our reference standard for the following reasons: it has a terminal functional group that accepts electrons and a dithienyl diketopyrropyrole (TDPP) core.…”