Machine Learning for Ultra High Throughput Screening of Organic Solar Cells: Solving the Needle in the Haystack Problem

Hußner, Markus; Pacalaj, Richard Adam; Olaf Müller‐Dieckert, Gerhard; Liu, Chao; Zhou, Zhisheng; Majeed, Nahdia; Greedy, Steve; Ramirez, Ivan; Li, Ning; Hosseini, Seyed Mehrdad; Uhrich, Christian; Brabec, Christoph Josef; Durrant, James Robert; Deibel, Carsten; MacKenzie, Roderick Charles Ian

Abstract

Over the last two decades the organic solar cell community has synthesized tens of thousands of novel polymers and small molecules in the search for an optimum light harvesting material. These materials are often crudely evaluated simply by measuring the current–voltage (JV) curves in the light to obtain power conversion efficiencies (PCEs). Materials with low PCEs are quickly disregarded in the search for higher efficiencies. More complex measurements such as frequency/time domain characterization that could explain why the material performed as it is often not performed as they are too time consuming/complex. This limited feedback forced the field to advance using a more or less random walk of material development and has significantly slowed progress. Herein, a simple technique based on machine learning that can quickly and accurately extract recombination time constants and charge carrier mobilities as a function of light intensity simply from light/dark JV curves alone. This technique reduces the time to fully analyze a working cell from weeks to seconds and opens up the possibility of not only fully characterizing new devices as they are fabricated, but also data mining historical data sets for promising materials the community has overlooked.

Citation

Hußner, M., Pacalaj, R. A., Olaf Müller‐Dieckert, G., Liu, C., Zhou, Z., Majeed, N., …MacKenzie, R. C. I. (2023). Machine Learning for Ultra High Throughput Screening of Organic Solar Cells: Solving the Needle in the Haystack Problem. Advanced Energy Materials, https://doi.org/10.1002/aenm.202303000

• Journal Article Type: Article
• Acceptance Date: Oct 13, 2023
• Online Publication Date: Dec 3, 2023
• Publication Date: Dec 3, 2023
• Deposit Date: Jan 5, 2024
• Publicly Available Date: Jan 5, 2024
• Journal: Advanced Energy Materials
• Print ISSN: 1614-6832
• Publisher: Wiley-VCH Verlag
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1002/aenm.202303000
• Keywords: solar, machine learning, drift diffusion, organic photovoltaic
• Public URL: https://durham-repository.worktribe.com/output/1985178