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Data-Driven Materials Discovery from Large Chemistry Spaces
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DOI：10.1016/j.matt.2020.07.010.png)
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Abstract
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Materials discovery often triggers new technological innovations and is therefore an exciting topic in materials research. In order to search a large chemistry space, however, lengthy trial-and-error testing is required. Recently, a Canadian research team devised a novel representation scheme for perovskite alloys. Combined with machine-learning methods, it enables efficient exploration of a large chemistry space with reasonable accuracy.
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