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Recent advances in machine learning (ML) have enabled predictive programs for photovoltaic characterization, optimization, and materials discovery. Despite these advances, the standard photovoltaic materials development workflow still involves manually performing multiple characterization techniques on every new device, requiring significant time and expenditures. One barrier to ML implementation is that most models reported to date are trained on computer simulated data, due to the difficulty in experimentally collecting the massive data sets needed for model training, limiting the ability to assess the limitations and validity of these methods, as well as to access new potential physical mechanisms absent in simulations. Herein, several neural networks trained on experimental data from PbS colloidal quantum dot thin‐film solar cells are introduced. These models predict multiple, complex materials properties, including carrier mobility, relative photoluminescence intensity, and electronic trap‐state density, from a single, simple measurement: illuminated current–voltage curves. The measurement system considers the spatial distribution of the materials parameters to gather and predict large amounts of data by treating an inhomogeneous device as a series of thousands of micro‐devices, a novel feature compared to existing solutions. This model can be extended to other materials and devices, accelerating development times for new optoelectronic technologies.
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Predicting Materials Parameters in Colloidal Quantum Dot Photovoltaic Devices Using Machine Learning Models Trained On Experimental Data
Numerous characterization techniques have been developed over the last century, which have advanced progress on the development of a variety of photovoltaic technologies. However, this multitude of techniques leads to increasing experimental costs and complexity. It would be useful to have an approach that does not require the time commitment or operation costs to directly learn and implement every new measurement technique. Herein, we explore several machine learning (ML) models that output complex materials parameters, such as electronic trap state density, solely using illuminated current-voltage curves. This greatly reduces both the complexity and cost of the characterization process. Current-voltage curves were chosen as the only input to our models because this type of measurement is relatively simple to perform and most photovoltaic research labs already collect this information on all devices. We compare several different ML network architectures, all of which are trained on experimental data from PbS colloidal quantum dot thin film solar cells. We predict values for underlying materials parameters and compare them to experimentally measured results.
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- Award ID(s):
- 1807342
- PAR ID:
- 10412778
- Date Published:
- Journal Name:
- 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC)
- Page Range / eLocation ID:
- 0862 to 0866
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/PVSC48317.2022.9938867
