The article Ki solves solar puzzles-for more efficient perovsky solar cells, first appeared at the online magazine Basic Thinking. You can start the day well every morning via our newsletter update.
Researchers have used a AI to decipher the atomic instability of perovskit materials. The results could help produce more efficient perovsky solar cells.
The global need for electricity is growing rapidly. Therefore, it requires better solar cells that are not only thin and flexible, but also deliver a lot of electricity. So-called Halogenid Perskite could enable this.
This is a promising material for the next generation solar cells. But there is a problem: So far, researchers have not known how these materials work in detail. Especially under extreme conditions (such as cold). Now have Researchers from Sweden Get important insights with the help of artificial intelligence.
The scientists at Chalmers University of Technology in Sweden examined a particularly complex material from the Perowskit Group: Formamidinium-Blueiodide (Fapbi₃). This has particularly good electronic properties. In addition, it is considered one of the best candidates for solar cells that one day could not only cover roofs, but also smartphones and entire building facades.
Perovskit solar cells through AI better than with supercomputers
The problem with it is that it is very unstable and quickly break down. In order to solve the problem, it first needs an understanding of how the material works on a nuclear level. So far, it has been particularly complex to model these perovsky materials on the computer because they are strongly attached and their organic components (so-called FAC cations) are constantly rotating.
It takes a lot of computing power and takes a long time on normal supercomputers. That is why the researchers picked up a trick: they combined their classic simulation methods with machine learning.
The AI basically learned to recognize a nuclear potential, and is much more precise than the old models. This enabled researchers to carry out simulations that thousands of times ran longer than before and use models with millions of atoms instead of only hundreds. This digital upgrade helped you to solve a basic puzzle. They identified the structure of the material at low temperatures, the so-called “gamma phase”.
The frozen chaos
What the team found out was quite interesting: the material does not reach its most stable condition when cooling, but sticks to a kind of frozen “metastable condition”. It is as if the formamidinium molecules get stuck and don’t manage to turn into the “right” final position.
This happens because a high energy career prevents the molecules from switching to its most stable state. The researchers were able to show that this frozen condition matches the one that they also observed in real experiments.
The findings are important. Because if researchers don’t know why a material is unstable, they cannot improve it. Now that the researchers have deciphered the atomic structure of the unstable phase, they can work specifically to prevent this instability by adding other perovskite.
According to study director Julia Wiktor, these new simulation methods can answer questions that were insoluble a few years ago. So the AI not only helped us to solve a matter of matter, but it is also the key to the super-efficient solar cells of tomorrow.
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The article Ki solves solar puzzles-for more efficient perovskit solar cells, first appeared on Basic Thinking. Follow us too Google News and Flipboard Or subscribe to our update newsletter.
As a tech industry expert, I am excited about the potential of AI to solve complex puzzles in the solar industry, specifically when it comes to improving the efficiency of perovskite solar cells. These cells have shown great promise in recent years due to their high efficiency and low cost, but there are still challenges that need to be overcome in order to maximize their potential.
AI can play a crucial role in this process by analyzing vast amounts of data and identifying patterns that may not be apparent to human researchers. By utilizing machine learning algorithms, AI can help researchers optimize the design and manufacturing processes of perovskite solar cells, leading to significant improvements in efficiency and performance.
Furthermore, AI can also assist in the development of new materials and technologies that could further enhance the capabilities of perovskite solar cells. By leveraging AI’s computational power and analytical capabilities, researchers can accelerate the pace of innovation in the solar industry and bring us closer to achieving a clean and sustainable energy future.
Overall, the integration of AI into the solar industry holds great promise for advancing the development of perovskite solar cells and other renewable energy technologies. By harnessing the power of AI, we can unlock new possibilities and drive towards more efficient and cost-effective solar solutions.
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