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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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Title: Machine Learning for Ultra High Throughput Screening of Organic Solar Cells: Solving the Needle in the Haystack Problem.
Authors: Hußner, Markus1 (AUTHOR), Pacalaj, Richard Adam2 (AUTHOR), Olaf Müller‐Dieckert, Gerhard3 (AUTHOR), Liu, Chao4,5 (AUTHOR), Zhou, Zhisheng6 (AUTHOR), Majeed, Nahdia7 (AUTHOR), Greedy, Steve7 (AUTHOR), Ramirez, Ivan8 (AUTHOR), Li, Ning4,5,6 (AUTHOR), Hosseini, Seyed Mehrdad8 (AUTHOR), Uhrich, Christian8 (AUTHOR), Brabec, Christoph Josef4,5 (AUTHOR), Durrant, James Robert2,9 (AUTHOR), Deibel, Carsten3 (AUTHOR), MacKenzie, Roderick Charles Ian1 (AUTHOR) roderick.mackenzie@durham.ac.uk
Source: Advanced Energy Materials. 1/19/2024, Vol. 14 Issue 3, p1-10. 10p.
Subject Terms: *High throughput screening (Drug development), *Solar cells, *Machine learning, *Photovoltaic power systems, *Charge carrier mobility, *Data mining, *Open-circuit voltage
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. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 174912552
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Machine Learning for Ultra High Throughput Screening of Organic Solar Cells: Solving the Needle in the Haystack Problem.
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  Data: <searchLink fieldCode="AR" term="%22Hußner%2C+Markus%22">Hußner, Markus</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pacalaj%2C+Richard+Adam%22">Pacalaj, Richard Adam</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Olaf+Müller‐Dieckert%2C+Gerhard%22">Olaf Müller‐Dieckert, Gerhard</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Chao%22">Liu, Chao</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Zhisheng%22">Zhou, Zhisheng</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Majeed%2C+Nahdia%22">Majeed, Nahdia</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Greedy%2C+Steve%22">Greedy, Steve</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ramirez%2C+Ivan%22">Ramirez, Ivan</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Ning%22">Li, Ning</searchLink><relatesTo>4,5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hosseini%2C+Seyed+Mehrdad%22">Hosseini, Seyed Mehrdad</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Uhrich%2C+Christian%22">Uhrich, Christian</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Brabec%2C+Christoph+Josef%22">Brabec, Christoph Josef</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Durrant%2C+James+Robert%22">Durrant, James Robert</searchLink><relatesTo>2,9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Deibel%2C+Carsten%22">Deibel, Carsten</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22MacKenzie%2C+Roderick+Charles+Ian%22">MacKenzie, Roderick Charles Ian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> roderick.mackenzie@durham.ac.uk</i>
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  Data: *<searchLink fieldCode="DE" term="%22High+throughput+screening+%28Drug+development%29%22">High throughput screening (Drug development)</searchLink><br />*<searchLink fieldCode="DE" term="%22Solar+cells%22">Solar cells</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Photovoltaic+power+systems%22">Photovoltaic power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Charge+carrier+mobility%22">Charge carrier mobility</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br />*<searchLink fieldCode="DE" term="%22Open-circuit+voltage%22">Open-circuit voltage</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
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        Value: 10.1002/aenm.202303000
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        Text: English
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      – SubjectFull: High throughput screening (Drug development)
        Type: general
      – SubjectFull: Solar cells
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      – SubjectFull: Machine learning
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      – SubjectFull: Photovoltaic power systems
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      – SubjectFull: Charge carrier mobility
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      – SubjectFull: Data mining
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      – SubjectFull: Open-circuit voltage
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              Text: 1/19/2024
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