Filter News
Area of Research
News Topics
- (-) Mathematics (1)
- (-) Transportation (33)
- 3-D Printing/Advanced Manufacturing (41)
- Advanced Reactors (9)
- Artificial Intelligence (19)
- Big Data (12)
- Bioenergy (22)
- Biology (23)
- Biomedical (15)
- Biotechnology (6)
- Buildings (13)
- Chemical Sciences (7)
- Clean Water (13)
- Climate Change (11)
- Composites (8)
- Computer Science (65)
- Coronavirus (10)
- Critical Materials (4)
- Cybersecurity (10)
- Decarbonization (7)
- Energy Storage (30)
- Environment (58)
- Exascale Computing (4)
- Frontier (6)
- Fusion (13)
- Grid (16)
- High-Performance Computing (19)
- Isotopes (13)
- ITER (4)
- Machine Learning (6)
- Materials (32)
- Materials Science (42)
- Mercury (4)
- Microscopy (15)
- Molten Salt (1)
- Nanotechnology (16)
- National Security (7)
- Net Zero (1)
- Neutron Science (36)
- Nuclear Energy (23)
- Physics (9)
- Polymers (7)
- Quantum Computing (5)
- Quantum Science (22)
- Security (5)
- Space Exploration (8)
- Statistics (1)
- Summit (18)
- Sustainable Energy (43)
Media Contacts
![Transportation Energy Data Book Edition 37](/sites/default/files/styles/list_page_thumbnail/public/2019-03/Transportation-Logging_the_miles_ORNL_0.jpg?h=ade3edc7&itok=wGiEijHl)
Oak Ridge National Laboratory’s latest Transportation Energy Data Book: Edition 37 reports that the number of vehicles nationwide is growing faster than the population, with sales more than 17 million since 2015, and the average household vehicle travels more than 11,000 miles per year.
![Laminations such as these are compiled to form the core of modern electric vehicle motors. ORNL has developed a software toolkit to speed the development of new motor designs and to improve the accuracy of their real-world performance.](/sites/default/files/styles/list_page_thumbnail/public/2019-02/Motors_OeRSTED_0.jpg?h=af53702d&itok=mT24R4WI)
Oak Ridge National Laboratory scientists have created open source software that scales up analysis of motor designs to run on the fastest computers available, including those accessible to outside users at the Oak Ridge Leadership Computing Facility.
![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.
![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.