Scalable machine learning models for predicting quantum transport in disordered 2D hexagonal materials

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A modern jet has an array of sensors to help it contend with bad weather. Magnetometers track the plane’s direction; gyroscopes help calculate its pitch and roll; accelerometers detect its changes in speed; Doppler radar measures the distance to the storm and how quickly it’s moving. In the early nineties, Larry Cornman developed a program that could sift through some of that sensor data to measure the air’s turbulence in real time. Atmospheric scientists call this the “eddy dissipation rate,” or E.D.R. It’s usually scored between 0 and 1—calm to severe.

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