AI on its own may not be as useful for discovering new materials as Google's DeepMind team has suggested.
GNoMe has expanded the number of materials available to science by discovering crystalline structures,"many of which escaped previous human chemical intuition," the authors claim. "Of its 2.2 million predictions, 380,000 are the most stable, making them promising candidates for experimental synthesis. Among these candidates are materials that have the potential to develop future transformative technologies ranging from superconductors, powering supercomputers, and next-generation batteries to boost the efficiency of electric vehicles.
The two UC Santa Barbara boffins argue the DeepMind predictions"are solely of crystalline inorganic compounds and should be described as such, rather than using the more generic label 'material.'" They note that there are many more types of materials into which GNoME did not delve, such as polymers, glasses, metal−organic frameworks, heterostructures, and composites.
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