Proceedings of International Conference on Hybrid and Organic Photovoltaics (HOPV24)
DOI: https://doi.org/10.29363/nanoge.hopv.2024.225
Publication date: 6th February 2024
Machine learning (ML) is a powerful tool to accelerate the development of halide perovskite materials and devices. Because this burgeoning class of material for photovoltaics entails a colossal chemical composition space, ML is very suitable to replace the conventional trial-and-error approach used in their characterization. Thus, there has been a pressing need within the materials research community to identify ML models that can be implemented to inform the physical and chemical behavior of the perovskites. We apply ML models varying from echo state networks to statistical models to classify and predict physical properties such as hole transport layer electrical conductivity, halide perovskite photoluminescence response, the power conversion efficiency of photovoltaic devices, etc. Specifically, we use in situ environmental optical measurements to predict the optical behavior of Cs-FA perovskites for 50+ hours, upon materials’ exposure to moisture. Here, we compare linear regression, echo state network, and seasonal auto-regressive integrated moving average with eXogenous regressor algorithms and attain accuracy of >90% for the latter. Our high-throughput measurements and ML-supported analyses validate the potential of ML to detect and forecast hybrid perovskites’ response with a variety of chemical compositions.
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International Conference on Hybrid and Organic Photovoltaics
International Conference on Hybrid and Organic Photovoltaics (HOPV) is celebrated yearly in May. The main topics are the development, function and modeling of materials and devices for hybrid and organic solar cells. The field is now dominated by perovskite solar cells but also other hybrid technologies, as organic solar cells, quantum dot solar cells, and dye-sensitized solar cells and their integration into devices for photoelectrochemical solar fuel production.
Asia-Pacific International Conference on Perovskite, Organic Photovoltaics and Optoelectronics
The main topics of the Asia-Pacific International Conference on Perovskite, Organic Photovoltaics and Optoelectronics (IPEROP) are discussed every year in Asia-Pacific for gathering the recent advances in the fields of material preparation, modeling and fabrication of perovskite and hybrid and organic materials. Photovoltaic devices are analyzed from fundamental physics and materials properties to a broad set of applications. The conference also covers the developments of perovskite optoelectronics, including light-emitting diodes, lasers, optical devices, nanophotonics, nonlinear optical properties, colloidal nanostructures, photophysics and light-matter coupling.
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