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Solar Cells and Relevant Machine Learning

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Machine Learning for Advanced Functional Materials

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Abstract

Machine learning (ML) and data science is the most emerging computation tool which has been recently incorporated in emerging fields of materials science and engineering including but not limited to solar cells. It helps us to optimize materials and their photovoltaic performance for various types of solar cells through algorithms and models, which is easy, cost-efficient, and rapid compared to conventional programming methods. Although the family of solar cells has been classified into various types based on their generations, however, the basic two types (i.e., organic, and inorganic solar cells) are more specific owing to the contrast in their materials, fabrication techniques, and corresponding characterizations. A large number of materials can be used for developing photoanode/photocathode in solar cells; however, it is too difficult and complex to design the most proficient one practically. In this chapter, we will comprehensively review ML about organic and inorganic solar cells, making a discussion about the use of machine learning, various classes of machine learning, common algorithms, and basic steps for ML. A detailed discussion about specific types of ML for solar cells and the application of ML for the prediction of suitable materials, optimization of device structure and fabrication processes, and reconstruction of measured data for solar cells are given. In the end, we shall cover the current research status and future challenges, and expected progress of ML, and will propose suggestions that can enhance the usefulness of machine learning.

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Authors and Affiliations

  1. Department of Materials Science and Engineering, Institute of Space Technology, Islamabad, 44000, Pakistan

    Muhammad Abdul Basit, Muhammad Aanish Ali & Mamoona Yasmeen

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  1. Muhammad Abdul Basit
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  2. Muhammad Aanish Ali
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  3. Mamoona Yasmeen
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Correspondence to Muhammad Abdul Basit .

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Editors and Affiliations

  1. University of Sao Paulo, Sao Carlos Institution of Physics, Grupo de Polimeros, São Paulo, Brazil

    Nirav Joshi

  2. Department of Civil Engineering Materials and Structures, Indian Institute of Technology Jammu, Jammu, India

    Vinod Kushvaha

  3. Proof of Concept and Innovation Group, Stanley Black and Decker (United States), Atlanta, GA, USA

    Priyanka Madhushri

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Basit, M.A., Aanish Ali, M., Yasmeen, M. (2023). Solar Cells and Relevant Machine Learning. In: Joshi, N., Kushvaha, V., Madhushri, P. (eds) Machine Learning for Advanced Functional Materials. Springer, Singapore. https://doi.org/10.1007/978-981-99-0393-1_1

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