Add-on for High Throughput Screening in Material Discovery for Organic Electronics: “Tagging” Molecules to Address the Device Considerations

Document Type : Original Article

Authors

1 Department of Organic Colorants, Institute for Color Science and Technology, Tehran, Iran

2 Center of Excellence for Color Science and Technology, Institute for Color Science and Technology, P.O. Box: 654-16765, Tehran, Iran

3 Petrochemical Research and Technology Company (NPC-rt), National Petrochemical Company (NPC), Tehran, Iran

4 Department of Resin and Additives, Institute for Color Science and Technology, Tehran, Iran

Abstract

This work reflects the worth of intelligent modeling in controlling the nanostructure morphology in manufacturing organic bulk heterojunction (BHJ) solar cells. It suggests the idea of screening the pool of material design possibilities inspired by machine learning. To fulfill this goal, a set of experimental data on a BHJ solar cell with a donor structure of diketopyrrolopyrrole (DDP) and backbone of benzothiadiazole (BT) are fed into a home-written artificial neural network (ANN)/genetic algorithm (GA) hybrid code to optimize film-casting parameters. The annealing temperature, spin coating spin rate, and donor/acceptor ratio taken from available literature are applied to give the machine chance of learning trends in the power conversion efficiency (PCE). DPP-BT structures virtually born in the mind of machine are then screened for resemblance survey to receive a tag of desired characteristic. The results enable device manufacturers to identify the sensitivity of designed molecules to specific film casting conditions, while homologous structures may result in similar responses against design variables.

Keywords

References

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Progress in Color, Colorants and Coatings
Volume 12, Issue 2
May 2019
Pages 107-120

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