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Solar photovoltaic (PV) technology has merged as an efficient and versatilemethod for converting the Sun's vast energy into electricity. Innovation indeveloping new materials and solar cell architectures is required to ensurelightweight, portable, and flexible miniaturized electronic devices operate forlong periods with reduced battery demand. Recent advances in biomedicalimplantable and wearable devices have coincided with a growing interest inefficient energy-harvesting solutions. Such devices primarily rely onrechargeable batteries to satisfy their energy needs. Moreover, ArtificialIntelligence (AI) and Machine Learning (ML) techniques are touted as gamechangers in energy harvesting, especially in solar energy materials. In thisarticle, we systematically review a range of ML techniques for optimizing theperformance of low-cost solar cells for miniaturized electronic devices. Oursystematic review reveals that these ML techniques can expedite the discoveryof new solar cell materials and architectures. In particular, this reviewcovers a broad range of ML techniques targeted at producing low-cost solarcells. Moreover, we present a new method of classifying the literatureaccording to data synthesis, ML algorithms, optimization, and fabricationprocess. In addition, our review reveals that the Gaussian Process Regression(GPR) ML technique with Bayesian Optimization (BO) enables the design of themost promising low-solar cell architecture. Therefore, our review is a criticalevaluation of existing ML techniques and is presented to guide researchers indiscovering the next generation of low-cost solar cells using ML techniques.

Machine learning for accelerating the discovery of high performance low-cost solar cells: a systematic review