Authors: Hoon Jeong Lee | Arlene Chiu | Yida Lin | Sreyas Chintapalli | Serene Kamal | Susanna M. Thon
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

(Invited) Parameter Prediction in Colloidal Quantum Dot Solar Cells Using Residual Neural Networks Trained on Experimental Data Colloidal quantum dots (CQDs) are an attractive third-generation material for photovoltaics, especially as infrared materials for multi-junction solar cells. Characterization and materials parameter prediction both play a critical role in the rising efficiencies in this field. However, there are numerous materials properties that need to be measured in order to fully characterize a device, leading to high research costs and slow development times. Here, we leverage recent advancements in machine learning (ML), along with a recently-demonstrated multimodal spectral characterization method [1], to predict complex materials parameters in CQD solar cells from simple current density-voltage (JV) curves, using an algorithm trained on experimental data.[2] Past work ML prediction of materials properties has focused on using simulated data due to the difficulty in collecting enough experimental data to fully train an algorithm. While it is not feasible to fabricate thousands of devices in a typical research lab, it is possible to measure several thousand points on a single device. We used a micrometer-resolution 2D characterization method with millimeter-scale field of view to acquire enough training data using only a handful of devices, while learning the spatial relationships between materials parameters. Photoluminescence, trap state density, transient photovoltage, transient photocurrent, and carrier mobility were predicted (Figure 1) by five different residual neural networks, each using a ResNet block and containing over 300k learnable parameters. The design of the networks were based on the ResNet50 architecture [3]. We also implemented a novel neighborhood approach to reduce errors and account for spatial correlations in device behavior, leading to insights into transport mechanisms and correlation lengths in CQD thin films. This method could be used to study a wide range of material systems and devices, ultimately leading to a universal prediction model that greatly simplifies the characterization process for optoelectronic devices and accelerates development times. References [1] Y. Lin, T. Gao, X. Pan, M. Kamenetska &amp; S. M. Thon, Local Defects in Colloidal Quantum Dot Thin Films Measured via Spatially-Resolved Multi-Modal Optoelectronic Spectroscopy . Advanced Materials 32 , 1906602 (2020). [2] H. J. Lee, A. Hofelmann, Y. Lin, &amp; S. M. Thon, Predicting Materials Parameters in Colloidal Quantum Dot Photovoltaic Devices Using Machine Learning Models Trained On Experimental Data . 2022 IEEE 49th Photovoltaic Specialists Conference, 0862-0866 (2022). [3] O. Elharrouss, N. Almaadeed, S. Al-Maadeed, &amp; Y. Akbari, Image inpainting: A review . Neural Processing Letters 51 , 2007–2028 (2020). Figure 1 Leverage (statistics) ; Materials science ; Computer science ; Photocurrent ; Photovoltaics ; Characterization (materials science) ; Quantum dot ; Artificial neural network ; Artificial intelligence ; Photovoltaic system ; Optoelectronics ; Nanotechnology ; Electronic engineering ; Electrical engineering ; Engineering Machine Learning in Materials Science