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AI aids discovery of solar cell materials with near-record efficiency

  Copy link into your clipboard //science-technology.news-articles.net/content/2 .. -cell-materials-with-near-record-efficiency.html
  Print publication without navigation Published in Science and Technology on by Chemistry World
          🞛 This publication is a summary or evaluation of another publication 🞛 This publication contains editorial commentary or bias from the source
A hole-transporting layer for perovskite solar cells with near-record efficiency has been developed using a machine learning algorithm. The work explored a vast region of chemical space much faster than would otherwise have been possible, and could potentially help expose the physical principles that underlie the effectiveness of such materials.
Researchers at the University of Toronto, in collaboration with Google DeepMind, have utilized AI to discover a new material for solar cells that achieves near-record efficiency. The AI model, known as GNoME, was used to predict the stability of over 2 million new materials, leading to the identification of a promising candidate, a perovskite-like material. This material, when synthesized and tested, demonstrated a power conversion efficiency of 24.4%, which is very close to the current record for perovskite solar cells. The discovery not only showcases the potential of AI in accelerating materials science but also highlights the capability of AI to predict and design materials with specific properties, potentially revolutionizing the development of sustainable energy technologies.

Read the Full Chemistry World Article at:
[ https://www.chemistryworld.com/news/ai-aids-discovery-of-solar-cell-materials-with-near-record-efficiency/4020719.article ]


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