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ORNL scientists used commuting behavior data from East Tennessee to demonstrate how machine learning models can easily accept new data, quickly re-train themselves and update predictions about commuting patterns. Credit: April Morton/Oak Ridge National La

Oak Ridge National Laboratory geospatial scientists who study the movement of people are using advanced machine learning methods to better predict home-to-work commuting patterns.

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OAK RIDGE, Tenn., Jan. 31, 2019—A new electron microscopy technique that detects the subtle changes in the weight of proteins at the nanoscale—while keeping the sample intact—could open a new pathway for deeper, more comprehensive studies of the basic building blocks of life. 

Jon Poplawsky of Oak Ridge National Laboratory combines atom probe tomography (revealed by this LEAP 4000XHR instrument) with electron microscopy to characterize the compositions, structures, and functions of materials for energy and information technolog

Jon Poplawsky, a materials scientist at the Department of Energy’s Oak Ridge National Laboratory, develops and links advanced characterization techniques that improve our ability to see and understand atomic-scale features of diverse materials

As hurricanes formed in the Gulf Coast, ORNL activated a computing technique to quickly gather building structure data from Texas’ coastal counties. Credit: Mark Tuttle/Oak Ridge National Laboratory, U.S. Dept. of Energy

Geospatial scientists at Oak Ridge National Laboratory have developed a novel method to quickly gather building structure datasets that support emergency response teams assessing properties damaged by Hurricanes Harvey and Irma. By coupling deep learning with high-performance comp...