Over the last few decades, machine learning has revolutionized many sectors of society, with machines learning to drive cars, identify tumors and play chess -- often surpassing their human counterparts.
Now, a team of scientists based at the Okinawa Institute of Science and Technology Graduate University (OIST), the University of Munich and the CNRS at the University of Bordeaux have shown that machines can also beat theoretical physicists at their own game, solving complex problems just as accurately as scientists, but considerably faster.
In the study, recently published in Physical Review B, a machine learned to identify unusual magnetic phases in a model of pyrochlore -- a naturally-occurring mineral with a tetrahedral lattice structure. Remarkably, when using the machine, solving the problem took only a few weeks, whereas previously the OIST scientists needed six years.
"This feels like a really significant step," said Professor Nic Shannon, who leads the Theory of Quantum Matter (TQM) Unit at OIST. "Computers are now able to carry out science in a very meaningful way and tackle problems that have long frustrated scientists."
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