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![Researchers used machine learning methods on the ORNL Compute and Data Environment for Science, or CADES, to map vegetation communities in the Kougarok Watershed on the Seward Peninsula of Alaska. The colors denote different types of vegetation, such as w Researchers used machine learning methods on the ORNL Compute and Data Environment for Science, or CADES, to map vegetation communities in the Kougarok Watershed on the Seward Peninsula of Alaska. The colors denote different types of vegetation, such as w](/sites/default/files/styles/list_page_thumbnail/public/rs2019_highlight_plot_3d.png?itok=5bROV_ys)
A team of scientists led by Oak Ridge National Laboratory used machine learning methods to generate a high-resolution map of vegetation growing in the remote reaches of the Alaskan tundra.
![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 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](/sites/default/files/styles/list_page_thumbnail/public/study_area_one_dest_2.jpg?itok=2cWFkQvW)
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.
![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 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](/sites/default/files/styles/list_page_thumbnail/public/2018-P09428_0.jpg?itok=rCMBpuR3)
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
![Supercomputing-Memory_boost1.jpg Supercomputing-Memory_boost1.jpg](/sites/default/files/styles/list_page_thumbnail/public/Supercomputing-Memory_boost1.jpg?itok=dDR8CnYC)
Scientists at Oak Ridge National Laboratory and Hypres, a digital superconductor company, have tested a novel cryogenic, or low-temperature, memory cell circuit design that may boost memory storage while using less energy in future exascale and quantum computing applications.
![Picture2.png Picture2.png](/sites/default/files/styles/list_page_thumbnail/public/Picture2_1.png?itok=IV4n9XEh)
Oak Ridge National Laboratory scientists studying fuel cells as a potential alternative to internal combustion engines used sophisticated electron microscopy to investigate the benefits of replacing high-cost platinum with a lower cost, carbon-nitrogen-manganese-based catalyst.