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Machine Learning for Ultra High Throughput Screening of Organic Solar Cells: Solving the Needle in the Haystack Problem
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Richard Adam Pacalaj ,
Gerhard Olaf Müller‐Dieckert,
Chao Liu,
Zhisheng Zhou,
Nahdia Majeed,
Steve Greedy,
Ivan Ramirez,
Ning Li,
Seyed Mehrdad Hosseini,
Christian Uhrich,
Christoph Josef Brabec,
James Durrant,
Carsten Deibel,
Roderick Charles Ian MacKenzie
Advanced Energy Materials, Volume: 14, Issue: 3, Start page: 2303000
Swansea University Author: James Durrant
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DOI (Published version): 10.1002/aenm.202303000
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 o...
| Published in: | Advanced Energy Materials |
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| ISSN: | 1614-6832 1614-6840 |
| Published: |
Wiley
2024
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa70700 |
| 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. |
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| Keywords: |
drift diffusion, machine learning, organic photovoltaic, solar |
| College: |
Faculty of Science and Engineering |
| Funders: |
The authors thank Heliatek GmbH for funding MH's PhD through the EPSRC Centre for Doctoral Training in Renewable Energy Northeast Universities (ReNU). The authors also thank the Deutsche Forschungsgemeinschaft (DFG Research Unit FOR 5387 POPULAR, Project No. 461909888) for their support. R.A.P. would further like to acknowledge the support through the Centre for Processable Electronics CDT program as well as the ATIP project (grant number EP/L016702/1 and EP/T028513/1). This work was supported by the Engineering and Physical Sciences Research Council (grant number EP/S023836/1). |
| Issue: |
3 |
| Start Page: |
2303000 |