A new machine-learning algorithm based on a neural network can tell a topological phase of matter from a conventional one. A detailed characterization of phases of matter is at the forefront of research in condensed-matter and statistical physics. Although physicists have made incredible progress in the characterization of a wide variety of phases, the identification of novel topological phases remains challenging.
A new machine-learning algorithm based on a neural network can tell a topological phase of matter from a conventional one. A detailed characterization of phases of matter is at the forefront of research in condensed-matter and statistical physics. Although physicists have made incredible progress in the characterization of a wide variety of phases, the identification of novel topological phases remains challenging.
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