If everything moved 40,000 times faster, you could eat a fresh tomato three minutes after planting a seed. You could fly from New York to L.A. in half a second. And you'd have waited in line at airport security for that flight for 30 milliseconds.
Thanks to machine learning, designing materials for new, advanced technologies could accelerate that much.
A research team at Sandia National Laboratories has successfully used machine learning -- computer algorithms that improve themselves by learning patterns in data -- to complete cumbersome materials science calculations more than 40,000 times faster than normal.
Their results, published Jan. 4 in npj Computational Materials, could herald a dramatic acceleration in the creation of new technologies for optics, aerospace, energy storage and potentially medicine while simultaneously saving laboratories money on computing costs.
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