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Unused solar, wind, and hydroelectric power in the U.S. could support the exponential growth of transactions involving non-fungible tokens (NFTs), Cornell Engineering researchers have found.
With the use of integration of high-powered X-Rays, machine learning, and phase-retrieval algorithms, Cornell scientists showed the intricate nanotextures in thin-film materials, providing researchers with a novel, streamlined method to examining likely candidates for microelectronics and quantum computing, among other applications.
Cornell is in charge of a new $11.3 million center devoted to human-AI collaboration using mathematics as a common language, with artificial intelligence prepared to aid in fundamental scientific discoveries that will change the world.
Using a combination of high-powered X-rays, phase-retrieval algorithms and machine learning, Cornell researchers revealed the intricate nanotextures in thin-film materials, offering scientists a new, streamlined approach to analyzing potential candidates for quantum computing and microelectronics, among other applications.
Using a combination of high-powered X-rays, phase-retrieval algorithms and machine learning, Cornell researchers revealed the intricate nanotextures in thin-film materials, offering scientists a new, streamlined approach to analyzing potential candidates for quantum computing and microelectronics, among other applications.