Authors:

Anika Bhoopalam

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About Paper:

The development of new materials for low-cost and efficient solar cells has gained traction in the past few decades. One of these promising classes of materials is called perovskites. In addition to research efforts into new materials, recent efforts include exploring new structures such as tandem solar cells, which contain solar absorbers of various bandgaps to capture more of the solar spectrum. The goal of this project is to develop new low-bandgap (below 1.1 eV) perovskite solar absorber materials with the overarching goal of incorporation into tandem solar cells. To achieve this goal, computational and experimental methods are employed. The computational methods include analysis of machine learning (ML) model results as well as the use of density functional theory (DFT). The ML model was trained on a combination of experimental and computational perovskite results in a specific chemical space. The compounds predicted to be stable and display a low- bandgap were chosen for further analysis with DFT. DFT is a first-principles computational method that can accurately calculate material properties. The candidates from the ML model were analyzed with DFT. DFT gave detailed information about the band structure and stability of chosen compounds. Compounds that were predicted to have desirable thermodynamic and electrical properties were tested experimentally using thin film studies. From the film studies, the real-life bandgap and stability could be determined. In the end, promising low-bandgap compounds were identified and in the future, they could be fabricated into solar cells to see their performance in a real device. Keywords: Perovskite; Thin Film; DFT

Source:

Purdue University / 2024

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No topics listed

Co-authors:

Anika Bhoopalam