An international team of scientists has leveraged machine learning to develop perovskite solar cells with near-record efficiency. Their research, published in Science, details how the use of machine-learning algorithms helped identify new hole-transporting materials, which significantly improved the performance of perovskite solar cells.

In a solar cell, the hole-transporting layer plays a crucial role in carrying electron-hole pairs generated by a semiconductor after photon absorption. The efficiency of this transport is critical to the overall performance of the solar cell, making the choice of material for this layer highly important.

Until now, effective materials for this layer have primarily been discovered through experimentation with existing structures. However, the team used a novel approach, applying machine learning to identify new, more effective materials.

The researchers employed a machine-learning algorithm that analyzed 101 molecules from a database of over a million candidates. The team then synthesized test solar cells with these materials, using the results to train the algorithm further. After multiple rounds of testing, the algorithm identified 24 promising material candidates.

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These candidates were synthesized and incorporated into working solar cells. After extensive testing, the team discovered a hole-transporting material that allowed them to construct perovskite-based solar cells with efficiencies as high as 26.2%. While the current record for such cells stands at 26.7%, the team's work came very close to setting a new benchmark for efficiency.

The researchers also noted that several materials they tested showed near-optimal performance, suggesting their approach could yield even more promising candidates capable of pushing efficiencies higher in the future.