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 empirical 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 $1.8 \times 10^{12}$. 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 acceptor end groups. Future NFA designs should consider this strategy as an alternative to the current A-D-A$'$-D-A architecture.

Screening Efficient Tandem Organic Solar Cells with Machine Learning and Genetic Algorithms

“…In each generation after this cycle, the molecules are evaluated using a fitness function to predict their performance. Previous work in our group used these algorithms to design organic photovoltaic materials and infer new design rules. − Our most recent study designed unfused NFAs and the GA revealed interesting strategies such as the inclusion of vinylene bridges and broken conjugation backbones . With this evolutionary approach, chemical space can be searched quickly and efficiently.…”

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.…”

Alteration of the central core of a DF-PCIC chromophore to boost the photovoltaic applications of non-fullerene acceptor based organic solar cells

“…This sequence of the A-D components also agrees with our understanding of the push-pull effect mentioned above. 25 So, it seemed to us that NFAs with an A-D-D ′ -D-A conguration might perform better than their corresponding A-D-A ′ -D-A counterparts in OSCs. To test this assumption, the central electron-accepting core (A') of the 2,2'-((2Z,2 ′ Z)-(((2,5-diuoro-1,4-phenylene)bis(4,4-bis(2-ethylhexyl)-4H-cyclopenta[2,1-b:3,4b ′ ]dithiophene-6,2-diyl))bis(methanylylidene))bis(3-oxo-2,3dihydro-1H-indene-2,1-diylidene))dimalononitrile (DF-PCIC) molecule was replaced with different strongly conjugated and electron rich donor groups.…”

“…This sequence of the A–D components also agrees with our understanding of the push–pull effect mentioned above. 25…”