The integration of solar energy into our daily lives has become increasingly important as we seek to move towards sustainable energy sources. One of the key factors in making solar technology more efficient and cost-effective is finding materials that can efficiently convert sunlight into electricity. While silicon has been the dominant material in solar technology, there is a shift towards using perovskites due to their lower costs and simpler manufacturing processes. However, the challenge has been to find perovskites with the right “band gap” – a specific energy range that determines how efficiently a material can absorb sunlight and convert it into electricity without losing it as heat.

An EPFL research project led by Haiyuan Wang and Alfredo Pasquarello, in collaboration with researchers in Shanghai and Louvain-La-Neuve, has developed a method that combines advanced computational techniques with machine learning to search for optimal perovskite materials for photovoltaic applications. This innovative approach could potentially lead to the discovery of more efficient and cheaper solar panels, setting new standards in the solar industry.

The researchers started by developing a comprehensive dataset of band-gap values for 246 perovskite materials using advanced calculations based on hybrid functionals. These hybrid functionals are a sophisticated type of computation that includes electron exchange and goes beyond the conventional Density Functional Theory (DFT). By incorporating the material’s electronic polarization properties into their calculations, the accuracy of the band-gap predictions was significantly enhanced.

The team then developed a machine learning model trained on the dataset of 246 perovskites and applied it to a database of about 15,000 candidate materials for solar cells. This model was able to narrow down the search to the most promising perovskites based on their predicted band gaps and stability. As a result, the model identified 14 completely new perovskites, all with band gaps and high energy stability, making them excellent candidates for high-efficiency solar cells.

Implications for the Solar Industry

This research demonstrates the potential of using machine learning to streamline the discovery and validation of new photovoltaic materials. By lowering costs and accelerating the adoption of solar energy, this technology has the capacity to reduce our dependence on fossil fuels and make a significant impact in the global effort to combat climate change. The discovery of these new materials opens up exciting possibilities for the future of solar energy, paving the way for more efficient and accessible solar technology.

The combination of advanced computational techniques and machine learning has the potential to revolutionize the field of solar energy by enabling the discovery of new materials that can enhance the efficiency and cost-effectiveness of solar technology. The work done by the EPFL research project represents a significant step forward in the quest for sustainable and renewable energy sources. By leveraging the power of machine learning, we can drive innovation in the solar industry and contribute to a more sustainable future for generations to come.

Chemistry

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