# Machine learning accelerates discovery of solar-cell perovskites

**URL:** <https://community.materialssquare.com/t/machine-learning-accelerates-discovery-of-solar-cell-perovskites/253>\
**Category:** News\
**Created:** [May 21, 2024, 12:22pm UTC](https://community.materialssquare.com/t/machine-learning-accelerates-discovery-of-solar-cell-perovskites/253 "2024-05-21T12:22:47Z")\
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**Author:** ![gabriele1](https://yyz1.discourse-cdn.com/flex003/user_avatar/community.materialssquare.com/gabriele1/32/20_2.png) [@gabriele1](https://community.materialssquare.com/u/gabriele1)\
**Post date:** [May 21, 2024, 12:22pm UTC](https://community.materialssquare.com/t/machine-learning-accelerates-discovery-of-solar-cell-perovskites/253/1 "2024-05-21T12:22:47Z")

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Researchers at EPFL have developed a method using machine learning to accelerate the discovery of perovskite materials for solar cells. By creating a high-quality dataset of band-gap values for 246 perovskite materials using advanced computational techniques, they trained a machine learning model to identify promising candidates from a database of 15,000 materials. This approach led to the discovery of 14 new perovskite materials with optimal properties for photovoltaic applications, potentially enhancing the efficiency and cost-effectiveness of solar panels.

For more details, read the full article here:

> **[Machine learning accelerates discovery of solar-cell perovskites](https://phys.org/news/2024-05-machine-discovery-solar-cell-perovskites.html)**
>
> An EPFL research project has developed a method based on machine learning to quickly and accurately search large databases, leading to the discovery of 14 new materials for solar cells.