![]() ![]() ![]() Initially, the algorithm would only generate nonsensical molecules, until we trained it to ensure that the molecules can be easily made in the lab. Reinhard Maurer, Professor of Computational Surface Chemistry and Interface Physics, at the University of Warwick, who led the study, said, "Our study developed an algorithm that can focus on a narrow range of electronic and optical properties, proposing new molecules for electronics applications. The corresponding molecules can also be designed to be small and suitable for mass production, promising to make newer generations of flexible TVs and screens cheaper, more flexible, lightweight and with higher color brilliance. In this study, led by the University of Warwick, the authors train the AI to search for molecules that can absorb and emit light of a certain color and are also easy to synthesize in the lab. Generative deep learning is able to create three-dimensional molecules that satisfy certain criteria much more efficiently than any previous technique. The use of AI enables a form of molecular design driven by the material's intended use. Finding molecules with optimal properties in the lab, through human trial and error, would otherwise be like finding a needle in a haystack. The application of generative AI in the context of chemistry is very recent and has the potential to reshape scientific discovery. Generative deep learning methods have become popular to create "deep fakes" of images and even computer-generated music. The algorithm is able to rapidly design millions of previously undiscovered molecules by building them atom by atom on a computer. In a study published in Nature February 6, scientists used a type of AI algorithm to create new molecules, suited for electronics that could come straight from a sci-fi movie. ![]()
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