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application of machine learning algorithms in improving nano-based solar cell technology
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نویسنده
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ghanbari motlagh saheb ,razi astaraei fateme ,hajihosseini mojtaba ,madani saeed
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منبع
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journal of ai and data mining - 2023 - دوره : 11 - شماره : 3 - صفحه:357 -374
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چکیده
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This study explores the potential use of machine learning (ml) techniques to enhance three types of nano-based solar cells. perovskites of methylammonium-free formamidinium (fa) and mixed cation-based cells exhibit a boosted efficiency when employing ml techniques. moreover, ml methods are utilized to identify optimal donor complexes, high blind temperature materials, and to advance the thermodynamic stability of perovskites. another significant application of ml in dye-sensitized solar cells (dsscs) is the detection of novel dyes, solvents, and molecules for improving the efficiency and performance of solar cells. some of these materials have increased cell efficiency, short-circuit current, and light absorption by more than 20%. ml algorithms to fine-tune network and plasmonic field bandwidths improve the efficiency and light absorption of surface plasmonic resonance (spr) solar cells. this study outlines the potential of ml techniques to optimize and improve the development of nano-based solar cells, leading to promising results for the field of solar energy generation and supporting the demand for sustainable and dependable energy.
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کلیدواژه
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machine learning ,optimization ,solar cell ,nano-based solar cell
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آدرس
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university of tehran, renewable energies and environment department, iran, university of tehran, renewable energies and environment department, iran, university of zagreb, faculty of electrical engineering and computing, laboratory for renewable energy systems, croatia, karlsruhe institute of technology (kit), germany
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پست الکترونیکی
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seyed.madani@kit.edu
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Authors
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