Perovskite solar cells are emerging as a flexible and sustainable alternative to traditional silicon-based solar cells.

Researchers at the Karlsruhe Institute of Technology (KIT) and the Helmholtz Institute Erlangen-Nürnberg (HI ERN) used a combination of artificial intelligence (AI) and automated high-throughput synthesis.

They have made a groundbreaking discovery.

Their innovative approach identified new organic molecules that significantly enhance the efficiency of perovskite solar cells, achieving results in just a few weeks that would have otherwise taken years. This breakthrough demonstrates the transformative potential of AI in accelerating materials discovery and optimizing energy technologies.

AI-Driven Discovery

The research team began with a database of approximately one million virtual molecules, each with unique structural properties. Using quantum mechanical methods, they analyzed 13,000 randomly selected molecules for key characteristics such as energy levels, polarity, and geometry.

From this subset, 101 molecules with the most diverse properties were synthesized using robotic systems at HI ERN. These molecules were then tested in solar cells to measure their efficiency.

The data from these experiments was used to train an AI model, which subsequently suggested 48 additional molecules for synthesis. This iterative process reduced the need for hundreds of thousands of experiments, saving significant time and resources.

Breakthrough Results

The AI-guided strategy led to the discovery of molecules that increased the efficiency of a reference solar cell by approximately two percentage points, reaching an impressive 26.2% efficiency. One of the key advantages of this method is its ability to identify unexpected high-performing materials, such as those containing chemical groups like amines.

which had been overlooked by traditional chemical intuition. The AI model also prioritized molecules with uncertain predictions, leading to surprising discoveries that might have been missed using conventional methods.

Broader Applications

This AI-driven approach is not limited to solar cells. It can be applied to other areas of materials research, such as the development of new battery materials or the optimization of entire components. The success of this strategy demonstrates the potential of AI to revolutionize materials science by accelerating the discovery of high-performance materials while minimizing costs.

For instance, the same methodology could be used to identify materials for energy storage, catalysis, or even pharmaceuticals, opening up new possibilities for innovation across industries.

Conclusion

The integration of AI and automated synthesis has proven to be a game-changer in the search for better photovoltaic materials. By leveraging AI to predict and prioritize promising molecules, researchers can achieve breakthroughs faster and more efficiently.

This innovative approach not only advances the field of perovskite solar cells but also paves the way for future discoveries in energy materials and beyond. As AI continues to evolve, its role in materials science will likely expand, offering new tools to tackle some of the world's most pressing challenges in energy and sustainability.