A machine learning algorithm designed to teach computers how to recognize photos, speech patterns, and hand-written digits has now been applied to a vastly different set a data: identifying different phases of condensed matter.
In a project half-jokingly called “Phasebook,” two Perimeter researchers showed that a neural network system – a standard part of today’s powerful artificial intelligence (AI) algorithms – can also identify phase transitions between states of matter. The research, published today in the journal Nature Physics, validates the idea that the relationship between theoretical physics and AI can be a fruitful, two-way exchange.
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