Case closed: Neutrons settle 40-year debate on enzyme for drug design
Filter News
News Type
News Topics
- (-) Emergency (1)
- 3-D Printing/Advanced Manufacturing (20)
- Advanced Reactors (3)
- Artificial Intelligence (26)
- Big Data (10)
- Bioenergy (22)
- Biology (29)
- Biomedical (7)
- Biotechnology (6)
- Buildings (14)
- Chemical Sciences (24)
- Clean Water (5)
- Climate Change (31)
- Composites (6)
- Computer Science (23)
- Coronavirus (4)
- Critical Materials (6)
- Cybersecurity (9)
- Decarbonization (30)
- Education (3)
- Energy Storage (21)
- Environment (43)
- Exascale Computing (15)
- Fossil Energy (2)
- Frontier (19)
- Fusion (9)
- Grid (16)
- High-Performance Computing (33)
- Hydropower (3)
- Irradiation (2)
- Isotopes (11)
- Machine Learning (15)
- Materials (59)
- Materials Science (16)
- Mathematics (2)
- Mercury (2)
- Microelectronics (2)
- Microscopy (7)
- Molten Salt (1)
- Nanotechnology (7)
- National Security (21)
- Net Zero (5)
- Neutron Science (32)
- Nuclear Energy (21)
- Partnerships (24)
- Physics (14)
- Polymers (4)
- Quantum Computing (12)
- Quantum Science (9)
- Renewable Energy (2)
- Security (3)
- Simulation (29)
- Software (1)
- Space Exploration (4)
- Summit (9)
- Sustainable Energy (17)
- Transportation (18)
Media Contacts
Lee's paper at the August conference in Bellevue, Washington, combined weather and power outage data for three states – Texas, Michigan and Hawaii – and used a machine learning model to predict how extreme weather such as thunderstorms, floods and tornadoes would affect local power grids and to estimate the risk for outages. The paper relied on data from the National Weather Service and the U.S. Department of Energy’s Environment for Analysis of Geo-Located Energy Information, or EAGLE-I, database.